From 1f4f695c7ecbfacaaada2ddd490bd474ea98855a Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Mon, 10 Nov 2025 12:01:30 +0000 Subject: [PATCH 01/17] adding vs module Signed-off-by: Nathaniel --- causalpy/__init__.py | 1 + causalpy/experiments/instrumental_variable.py | 19 +- causalpy/pymc_models.py | 68 +- .../tests/test_integration_pymc_examples.py | 32 + causalpy/variable_selection_priors.py | 591 ++++++++++++++++++ docs/source/_static/interrogate_badge.svg | 6 +- 6 files changed, 698 insertions(+), 19 deletions(-) create mode 100644 causalpy/variable_selection_priors.py diff --git a/causalpy/__init__.py b/causalpy/__init__.py index 5587fb3e..09384669 100644 --- a/causalpy/__init__.py +++ b/causalpy/__init__.py @@ -41,4 +41,5 @@ "RegressionKink", "skl_models", "SyntheticControl", + "variable_selection_priors", ] diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py index 001ce9af..ebd64675 100644 --- a/causalpy/experiments/instrumental_variable.py +++ b/causalpy/experiments/instrumental_variable.py @@ -52,6 +52,12 @@ class InstrumentalVariable(BaseExperiment): "eta": 2, "lkj_sd": 2, } + :param vs_prior_type : str or None, default=None + Type of variable selection prior: 'spike_and_slab', 'horseshoe', or None. + If None, uses standard normal priors. + :param vs_hyperparams : dict, optional + Hyperparameters for variable selection priors. Only used if vs_prior_type + is not None. Example -------- @@ -99,6 +105,8 @@ def __init__( formula: str, model=None, priors=None, + vs_prior_type=None, + vs_hyperparams=None, **kwargs, ): super().__init__(model=model) @@ -108,6 +116,8 @@ def __init__( self.formula = formula self.instruments_formula = instruments_formula self.model = model + self.vs_prior_type = (vs_prior_type,) + self.vs_hyperparams = vs_hyperparams or {} self.input_validation() y, X = dmatrices(formula, self.data) @@ -139,7 +149,14 @@ def __init__( } self.priors = priors self.model.fit( - X=self.X, Z=self.Z, y=self.y, t=self.t, coords=COORDS, priors=self.priors + X=self.X, + Z=self.Z, + y=self.y, + t=self.t, + coords=COORDS, + priors=self.priors, + vs_prior_type=vs_prior_type, + vs_hyperparams=vs_hyperparams, ) def input_validation(self): diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index 75dc14a7..18ef4264 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -26,6 +26,7 @@ from pymc_extras.prior import Prior from causalpy.utils import round_num +from causalpy.variable_selection_priors import VariableSelectionPrior class PyMCModel(pm.Model): @@ -604,7 +605,9 @@ class InstrumentalVariableRegression(PyMCModel): Inference data... """ - def build_model(self, X, Z, y, t, coords, priors): + def build_model( + self, X, Z, y, t, coords, priors, vs_prior_type=None, vs_hyperparams=None + ): """Specify model with treatment regression and focal regression data and priors :param X: A pandas dataframe used to predict our outcome y @@ -618,23 +621,47 @@ def build_model(self, X, Z, y, t, coords, priors): sigmas of both regressions :code:`priors = {"mus": [0, 0], "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}` + :param vs_prior_type: An optional string. Can be "spike_and_slab" + or "horseshoe" or "normal + :param vs_hyperparams: An optional dictionary of priors for the + variable selection hyperparameters + """ # --- Priors --- with self: self.add_coords(coords) - beta_t = pm.Normal( - name="beta_t", - mu=priors["mus"][0], - sigma=priors["sigmas"][0], - dims="instruments", - ) - beta_z = pm.Normal( - name="beta_z", - mu=priors["mus"][1], - sigma=priors["sigmas"][1], - dims="covariates", - ) + + # Create coefficient priors + if vs_prior_type: + # Use variable selection priors + vs_prior_treatment = VariableSelectionPrior( + vs_prior_type, vs_hyperparams + ) + vs_prior_outcome = VariableSelectionPrior(vs_prior_type, vs_hyperparams) + + beta_t = vs_prior_treatment.create_prior( + name="beta_t", n_params=Z.shape[1], dims="instruments", X=Z + ) + + beta_z = vs_prior_outcome.create_prior( + name="beta_z", n_params=X.shape[1], dims="covariates", X=X + ) + else: + # Use standard normal priors + beta_t = pm.Normal( + name="beta_t", + mu=priors["mus"][0], + sigma=priors["sigmas"][0], + dims="instruments", + ) + beta_z = pm.Normal( + name="beta_z", + mu=priors["mus"][1], + sigma=priors["sigmas"][1], + dims="covariates", + ) + sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2) chol, corr, sigmas = pm.LKJCholeskyCov( name="chol_cov", @@ -689,7 +716,18 @@ def sample_predictive_distribution(self, ppc_sampler="jax"): ) ) - def fit(self, X, Z, y, t, coords, priors, ppc_sampler=None): + def fit( + self, + X, + Z, + y, + t, + coords, + priors, + ppc_sampler=None, + vs_prior_type=None, + vs_hyperparams=None, + ): """Draw samples from posterior distribution and potentially from the prior and posterior predictive distributions. The fit call can take values for the @@ -703,7 +741,7 @@ def fit(self, X, Z, y, t, coords, priors, ppc_sampler=None): # sample_posterior_predictive() if provided in sample_kwargs. # Use JAX for ppc sampling of multivariate likelihood - self.build_model(X, Z, y, t, coords, priors) + self.build_model(X, Z, y, t, coords, priors, vs_prior_type, vs_hyperparams) with self: self.idata = pm.sample(**self.sample_kwargs) self.sample_predictive_distribution(ppc_sampler=ppc_sampler) diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py index e7795522..e3a1a1b9 100644 --- a/causalpy/tests/test_integration_pymc_examples.py +++ b/causalpy/tests/test_integration_pymc_examples.py @@ -676,6 +676,38 @@ def test_iv_reg(mock_pymc_sample): result.get_plot_data() +@pytest.mark.integration +def test_iv_reg_vs_prior(mock_pymc_sample): + df = cp.load_data("risk") + instruments_formula = "risk ~ 1 + logmort0" + formula = "loggdp ~ 1 + risk" + instruments_data = df[["risk", "logmort0"]] + data = df[["loggdp", "risk"]] + + result = cp.InstrumentalVariable( + instruments_data=instruments_data, + data=data, + instruments_formula=instruments_formula, + formula=formula, + model=cp.pymc_models.InstrumentalVariableRegression( + sample_kwargs=sample_kwargs + ), + vs_prior_type="spike_and_slab", + vs_hyperparams={"pi_alpha": 5}, + ) + result.model.sample_predictive_distribution(ppc_sampler="pymc") + assert isinstance(df, pd.DataFrame) + assert isinstance(data, pd.DataFrame) + assert isinstance(instruments_data, pd.DataFrame) + assert isinstance(result, cp.InstrumentalVariable) + assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"] + assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"] + with pytest.raises(NotImplementedError): + result.get_plot_data() + assert "gamma_beta_t" in result.model.named_vars + assert "pi_beta_t" in result.model.named_vars + + @pytest.mark.integration def test_inverse_prop(mock_pymc_sample): """Test the InversePropensityWeighting class.""" diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py new file mode 100644 index 00000000..5cac5013 --- /dev/null +++ b/causalpy/variable_selection_priors.py @@ -0,0 +1,591 @@ +# Copyright 2022 - 2025 The PyMC Labs Developers +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Generic variable selection priors for PyMC models using pymc-extras Prior class. + +This module provides reusable prior specifications that can be applied to any +PyMC model with coefficient vectors (beta parameters). Supports spike-and-slab +and horseshoe priors for automatic variable selection and shrinkage, built on +top of the pymc-extras Prior infrastructure. +""" + +from typing import Any, Dict, Optional, Union + +import numpy as np +import pymc as pm +import pytensor.tensor as pt +from pymc_extras.prior import Prior + + +def _relaxed_bernoulli_transform( + p: Union[float, pt.TensorVariable], temperature: float = 0.1 +): + """ + Transform function for relaxed (continuous) Bernoulli distribution. + + This provides a continuous approximation to a Bernoulli distribution, + useful for gradient-based inference. As temperature → 0, this approaches + a true binary distribution. + + Parameters + ---------- + p : float or PyMC variable + Probability parameter + temperature : float, default=0.1 + Temperature parameter (lower = more binary) + + Returns + ------- + function + Transform function that takes uniform random variable + """ + + def transform(u): + logit_p = pt.log(p) - pt.log(1 - p) + return pm.math.sigmoid((logit_p + pt.log(u) - pt.log(1 - u)) / temperature) + + return transform + + +class SpikeAndSlabPrior: + """ + Spike-and-slab prior using pymc-extras Prior class. + + Creates a mixture prior with a point mass at zero (spike) and a diffuse + normal distribution (slab), implemented as: + + β_j = γ_j × β_j^raw + + where γ_j ∈ [0,1] is a relaxed indicator and β_j^raw ~ N(0, σ_slab²). + + Parameters + ---------- + pi_alpha : float, default=2 + Beta prior alpha for selection probability + pi_beta : float, default=2 + Beta prior beta for selection probability + slab_sigma : float, default=2 + Standard deviation of slab (non-zero) component + temperature : float, default=0.1 + Relaxation parameter for binary approximation (lower = more binary) + dims : str or tuple, optional + Dimension names for the coefficient vector + + Example + ------- + >>> spike_slab = SpikeAndSlabPrior(dims="features") + >>> with pm.Model(): + ... beta = spike_slab.create_variable("beta") + """ + + def __init__( + self, + pi_alpha: float = 2, + pi_beta: float = 2, + slab_sigma: float = 2, + temperature: float = 0.1, + dims: Optional[Union[str, tuple]] = None, + ): + self.pi_alpha = pi_alpha + self.pi_beta = pi_beta + self.slab_sigma = slab_sigma + self.temperature = temperature + self.dims = dims if isinstance(dims, tuple) or dims is None else (dims,) + + def create_variable(self, name: str) -> pm.Deterministic: + """ + Create spike-and-slab variable. + + Parameters + ---------- + name : str + Name for the coefficient vector + + Returns + ------- + pm.Deterministic + Coefficient vector with spike-and-slab prior + """ + # Selection probability using Prior class + pi_prior = Prior("Beta", alpha=self.pi_alpha, beta=self.pi_beta) + pi = pi_prior.create_variable(f"pi_{name}") + + # Raw coefficients (slab component) using Prior class + slab_prior = Prior("Normal", mu=0, sigma=self.slab_sigma, dims=self.dims) + beta_raw = slab_prior.create_variable(f"{name}_raw") + + # Selection indicators using relaxed Bernoulli + # We use Uniform and transform it + u = pm.Uniform(f"gamma_{name}_u", 0, 1, dims=self.dims) + transform_fn = _relaxed_bernoulli_transform(pi, self.temperature) + gamma = pm.Deterministic(f"gamma_{name}", transform_fn(u), dims=self.dims) + + # Actual coefficients + return pm.Deterministic(name, gamma * beta_raw, dims=self.dims) + + +class HorseshoePrior: + """ + Regularized horseshoe prior using pymc-extras Prior class. + + Provides continuous shrinkage with heavy tails, allowing strong signals + to escape shrinkage while weak signals are dampened: + + β_j = τ · λ̃_j · β_j^raw + + where λ̃_j = √(c²λ_j² / (c² + τ²λ_j²)) is the regularized local shrinkage. + + Parameters + ---------- + tau0 : float, optional + Global shrinkage parameter. If None, computed from data. + nu : float, default=3 + Degrees of freedom for half-t prior on tau + c2_alpha : float, default=2 + InverseGamma alpha for regularization parameter + c2_beta : float, default=2 + InverseGamma beta for regularization parameter + dims : str or tuple, optional + Dimension names for the coefficient vector + + Example + ------- + >>> horseshoe = HorseshoePrior(dims="features") + >>> with pm.Model(): + ... beta = horseshoe.create_variable("beta") + """ + + def __init__( + self, + tau0: Optional[float] = None, + nu: float = 3, + c2_alpha: float = 2, + c2_beta: float = 2, + dims: Optional[Union[str, tuple]] = None, + ): + self.tau0 = tau0 + self.nu = nu + self.c2_alpha = c2_alpha + self.c2_beta = c2_beta + self.dims = dims if isinstance(dims, tuple) or dims is None else (dims,) + + def create_variable(self, name: str) -> pm.Deterministic: + """ + Create horseshoe variable. + + Parameters + ---------- + name : str + Name for the coefficient vector + + Returns + ------- + pm.Deterministic + Coefficient vector with horseshoe prior + """ + # Global shrinkage using Prior class + tau_prior = Prior("HalfStudentT", nu=self.nu, sigma=self.tau0 or 1.0) + tau = tau_prior.create_variable(f"tau_{name}") + + # Local shrinkage parameters using Prior class + lambda_prior = Prior("HalfCauchy", beta=1.0, dims=self.dims) + lambda_ = lambda_prior.create_variable(f"lambda_{name}") + + # Regularization parameter using Prior class + c2_prior = Prior("InverseGamma", alpha=self.c2_alpha, beta=self.c2_beta) + c2 = c2_prior.create_variable(f"c2_{name}") + + # Regularized local shrinkage + lambda_tilde = pm.Deterministic( + f"lambda_tilde_{name}", + pm.math.sqrt(c2 * lambda_**2 / (c2 + tau**2 * lambda_**2)), + dims=self.dims, + ) + + # Raw coefficients using Prior class + raw_prior = Prior("Normal", mu=0, sigma=1, dims=self.dims) + beta_raw = raw_prior.create_variable(f"{name}_raw") + + # Actual coefficients + return pm.Deterministic(name, beta_raw * lambda_tilde * tau, dims=self.dims) + + +class VariableSelectionPrior: + """ + Factory for creating variable selection priors on coefficient vectors. + + This class provides a unified interface for different types of variable + selection priors that can be applied to any beta coefficient in a PyMC model. + Built on top of pymc-extras Prior class for consistency and interoperability. + + Supported prior types: + - 'spike_and_slab': Mixture prior with near-zero spike and diffuse slab + - 'horseshoe': Continuous shrinkage with adaptive regularization + - 'normal': Standard normal prior (no selection, for comparison) + + Parameters + ---------- + prior_type : str + Type of prior: 'spike_and_slab', 'horseshoe', or 'normal' + hyperparams : dict, optional + Hyperparameters specific to the chosen prior type. If None, defaults are used. + + For 'spike_and_slab': + - pi_alpha: float (default=2) - Beta prior alpha for selection probability + - pi_beta: float (default=2) - Beta prior beta for selection probability + - slab_sigma: float (default=2) - SD of slab (non-zero) component + - temperature: float (default=0.1) - Relaxation parameter for binary approximation + + For 'horseshoe': + - tau0: float (default=None) - Global shrinkage, auto-computed if None + - nu: float (default=3) - Degrees of freedom for half-t prior on tau + - c2_alpha: float (default=2) - InverseGamma alpha for regularization + - c2_beta: float (default=2) - InverseGamma beta for regularization + + For 'normal': + - mu: float or array (default=0) - Prior mean + - sigma: float or array (default=1) - Prior SD + + Example + ------- + >>> import pymc as pm + >>> from variable_selection_priors import VariableSelectionPrior + >>> + >>> # Create spike-and-slab prior + >>> vs_prior = VariableSelectionPrior("spike_and_slab") + >>> + >>> with pm.Model() as model: + ... # Create coefficients with variable selection + ... beta = vs_prior.create_prior( + ... name="beta", + ... n_params=5, + ... dims="features", + ... X=X_train, # For computing tau0 in horseshoe + ... ) + """ + + def __init__(self, prior_type: str, hyperparams: Optional[Dict[str, Any]] = None): + """Initialize the variable selection prior factory.""" + self.prior_type = prior_type.lower() + self.hyperparams = hyperparams or {} + + if self.prior_type not in ["spike_and_slab", "horseshoe", "normal"]: + raise ValueError( + f"Unknown prior_type: {prior_type}. " + "Must be 'spike_and_slab', 'horseshoe', or 'normal'" + ) + + # Will be set when create_prior is called + self._prior_instance = None + + def _get_default_hyperparams( + self, n_params: int, X: Optional[np.ndarray] = None + ) -> Dict[str, Any]: + """ + Get default hyperparameters for the chosen prior type. + + Parameters + ---------- + n_params : int + Number of parameters (dimension of beta vector) + X : array-like, optional + Design matrix for computing data-adaptive defaults (horseshoe only) + + Returns + ------- + dict + Default hyperparameters + """ + if self.prior_type == "spike_and_slab": + return { + "pi_alpha": 2, + "pi_beta": 2, + "slab_sigma": 2, + "temperature": 0.1, + } + + elif self.prior_type == "horseshoe": + # Compute tau0 using rule of thumb from Piironen & Vehtari (2017) + if X is not None: + p = n_params + p0 = min(5.0, p / 2) # Expected number of nonzero coefficients + sigma_est = 1.0 + n = X.shape[0] + tau0 = (p0 / (p - p0)) * (sigma_est / np.sqrt(n)) + else: + # Fallback if no data provided + tau0 = 1.0 / np.sqrt(n_params) + + return { + "tau0": tau0, + "nu": 3, + "c2_alpha": 2, + "c2_beta": 2, + } + + else: # normal + return { + "mu": 0, + "sigma": 1, + } + + def create_prior( + self, + name: str, + n_params: int, + dims: Optional[Union[str, tuple]] = None, + X: Optional[np.ndarray] = None, + hyperparams: Optional[Dict[str, Any]] = None, + ) -> Union[pm.Deterministic, pm.Distribution]: + """ + Create the specified prior on a coefficient vector. + + This is the main method to use. It creates the appropriate prior type + based on the configuration and returns the PyMC variable. + + Parameters + ---------- + name : str + Name for the coefficient vector (e.g., 'beta', 'b', 'coef') + n_params : int + Number of parameters (length of coefficient vector) + dims : str or tuple, optional + Dimension name(s) for the coefficient vector + X : array-like, optional + Design matrix for computing data-adaptive hyperparameters + (used only for horseshoe priors) + hyperparams : dict, optional + Override default hyperparameters for this specific prior instance + + Returns + ------- + PyMC variable + The coefficient vector with the specified prior + + Example + ------- + >>> vs_prior = VariableSelectionPrior("horseshoe") + >>> with pm.Model() as model: + ... beta = vs_prior.create_prior( + ... "beta", n_params=10, dims="features", X=X_train + ... ) + """ + # Merge instance and call-specific hyperparameters + default_hp = self._get_default_hyperparams(n_params, X) + merged_hp = {**default_hp, **self.hyperparams} + if hyperparams: + merged_hp.update(hyperparams) + + # Normalize dims + if isinstance(dims, str): + dims = (dims,) + + # Create the appropriate prior + if self.prior_type == "spike_and_slab": + self._prior_instance = SpikeAndSlabPrior( + pi_alpha=merged_hp["pi_alpha"], + pi_beta=merged_hp["pi_beta"], + slab_sigma=merged_hp["slab_sigma"], + temperature=merged_hp["temperature"], + dims=dims, + ) + return self._prior_instance.create_variable(name) + + elif self.prior_type == "horseshoe": + self._prior_instance = HorseshoePrior( + tau0=merged_hp["tau0"], + nu=merged_hp["nu"], + c2_alpha=merged_hp["c2_alpha"], + c2_beta=merged_hp["c2_beta"], + dims=dims, + ) + return self._prior_instance.create_variable(name) + + else: # normal + # Use Prior class directly for normal + normal_prior = Prior( + "Normal", mu=merged_hp["mu"], sigma=merged_hp["sigma"], dims=dims + ) + return normal_prior.create_variable(name) + + def get_inclusion_probabilities( + self, idata, param_name: str, threshold: float = 0.5 + ) -> Dict[str, np.ndarray]: + """ + Extract variable inclusion probabilities from fitted model. + + Only applicable for spike-and-slab priors. Returns the posterior + probability that each coefficient is "selected" (non-zero). + + Parameters + ---------- + idata : arviz.InferenceData + Fitted model inference data + param_name : str + Name of the coefficient parameter (must match name in create_prior) + threshold : float, default=0.5 + Threshold for considering a variable "selected" + + Returns + ------- + dict + Dictionary with keys: + - 'probabilities': Array of inclusion probabilities per coefficient + - 'selected': Boolean array indicating which are selected + - 'gamma_mean': Mean of gamma (indicator) variables + + Raises + ------ + ValueError + If prior_type is not 'spike_and_slab' or gamma variables not found + + Example + ------- + >>> result = vs_prior.get_inclusion_probabilities(idata, "beta") + >>> print(f"Selected features: {result['selected']}") + >>> print(f"Inclusion probs: {result['probabilities']}") + """ + if self.prior_type != "spike_and_slab": + raise ValueError( + "Inclusion probabilities only available for 'spike_and_slab' priors" + ) + + gamma_name = f"gamma_{param_name}" + + if gamma_name not in idata.posterior: + raise ValueError( + f"Could not find '{gamma_name}' in posterior. " + f"Make sure you used the correct parameter name." + ) + + import arviz as az + + # Extract gamma values + gamma = az.extract(idata.posterior[gamma_name]) + + # Compute inclusion probabilities + probabilities = (gamma > threshold).mean(dim="sample").values + gamma_mean = gamma.mean(dim="sample").values + selected = probabilities > threshold + + return { + "probabilities": probabilities, + "selected": selected, + "gamma_mean": gamma_mean, + } + + def get_shrinkage_factors(self, idata, param_name: str) -> Dict[str, np.ndarray]: + """ + Extract shrinkage factors from horseshoe prior. + + Only applicable for horseshoe priors. Returns the effective shrinkage + applied to each coefficient: κ_j = τ · λ̃_j + + Parameters + ---------- + idata : arviz.InferenceData + Fitted model inference data + param_name : str + Name of the coefficient parameter + + Returns + ------- + dict + Dictionary with keys: + - 'shrinkage_factors': Array of shrinkage factors per coefficient + - 'tau': Global shrinkage parameter + - 'lambda_tilde': Regularized local shrinkage parameters + + Raises + ------ + ValueError + If prior_type is not 'horseshoe' or required variables not found + + Example + ------- + >>> result = vs_prior.get_shrinkage_factors(idata, "beta") + >>> print(f"Shrinkage factors: {result['shrinkage_factors']}") + """ + if self.prior_type != "horseshoe": + raise ValueError("Shrinkage factors only available for 'horseshoe' priors") + + import arviz as az + + tau_name = f"tau_{param_name}" + lambda_tilde_name = f"lambda_tilde_{param_name}" + + if tau_name not in idata.posterior: + raise ValueError(f"Could not find '{tau_name}' in posterior") + if lambda_tilde_name not in idata.posterior: + raise ValueError(f"Could not find '{lambda_tilde_name}' in posterior") + + # Extract components + tau = az.extract(idata.posterior[tau_name]) + lambda_tilde = az.extract(idata.posterior[lambda_tilde_name]) + + # Compute shrinkage factors + shrinkage_factors = (tau * lambda_tilde).mean(dim="sample").values + + return { + "shrinkage_factors": shrinkage_factors, + "tau": tau.mean().values, + "lambda_tilde": lambda_tilde.mean(dim="sample").values, + } + + +def create_variable_selection_prior( + prior_type: str, + name: str, + n_params: int, + dims: Optional[Union[str, tuple]] = None, + X: Optional[np.ndarray] = None, + hyperparams: Optional[Dict[str, Any]] = None, +) -> Union[pm.Deterministic, pm.Distribution]: + """ + Convenience function to create a variable selection prior in one call. + + This is a shorthand for creating a VariableSelectionPrior instance and + calling create_prior() in one step. + + Parameters + ---------- + prior_type : str + Type of prior: 'spike_and_slab', 'horseshoe', or 'normal' + name : str + Name for the coefficient vector + n_params : int + Number of parameters + dims : str or tuple, optional + Dimension name(s) + X : array-like, optional + Design matrix for data-adaptive hyperparameters + hyperparams : dict, optional + Custom hyperparameters + + Returns + ------- + PyMC variable + The coefficient vector with specified prior + + Example + ------- + >>> with pm.Model() as model: + ... X = pm.Data("X", X_train) + ... beta = create_variable_selection_prior( + ... "spike_and_slab", "beta", n_params=X_train.shape[1], dims="features" + ... ) + ... mu = pm.math.dot(X, beta) + """ + vs_prior = VariableSelectionPrior(prior_type, hyperparams) + return vs_prior.create_prior(name, n_params, dims, X) diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg index 392a876b..a00d0758 100644 --- a/docs/source/_static/interrogate_badge.svg +++ b/docs/source/_static/interrogate_badge.svg @@ -1,5 +1,5 @@ - interrogate: 96.2% + interrogate: 95.7% @@ -12,8 +12,8 @@ interrogate interrogate - 96.2% - 96.2% + 95.7% + 95.7% From db1e81d2caf76bbee4a5d136c877c11327d8bf01 Mon Sep 17 00:00:00 2001 From: Nathaniel Date: Thu, 20 Nov 2025 07:02:23 +0000 Subject: [PATCH 02/17] adding demo notebook Signed-off-by: Nathaniel --- causalpy/experiments/instrumental_variable.py | 14 +- causalpy/pymc_models.py | 8 + docs/source/notebooks/iv_vs_priors.ipynb | 1377 +++++++++++++++++ 3 files changed, 1397 insertions(+), 2 deletions(-) create mode 100644 docs/source/notebooks/iv_vs_priors.ipynb diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py index d24a54be..67852123 100644 --- a/causalpy/experiments/instrumental_variable.py +++ b/causalpy/experiments/instrumental_variable.py @@ -91,6 +91,16 @@ class InstrumentalVariable(BaseExperiment): ... formula=formula, ... model=InstrumentalVariableRegression(sample_kwargs=sample_kwargs), ... ) + >>> # With variable selection + >>> iv = cp.InstrumentalVariable( + ... instruments_data=instruments_data, + ... data=data, + ... instruments_formula=instruments_formula, + ... formula=formula, + ... model=InstrumentalVariableRegression(sample_kwargs=sample_kwargs), + ... vs_prior_type="spike_and_slab", + ... vs_hyperparams={"slab_sigma": 5.0}, + ... ) """ supports_ols = False @@ -115,7 +125,7 @@ def __init__( self.formula = formula self.instruments_formula = instruments_formula self.model = model - self.vs_prior_type = (vs_prior_type,) + self.vs_prior_type = vs_prior_type self.vs_hyperparams = vs_hyperparams or {} self.input_validation() @@ -142,7 +152,7 @@ def __init__( if priors is None: priors = { "mus": [self.ols_beta_first_params, self.ols_beta_second_params], - "sigmas": [1, 1], + "sigmas": [10, 10], "eta": 2, "lkj_sd": 1, } diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py index f31e147e..d0c04bfd 100644 --- a/causalpy/pymc_models.py +++ b/causalpy/pymc_models.py @@ -13,6 +13,7 @@ # limitations under the License. """Custom PyMC models for causal inference""" +import warnings from typing import Any, Dict import arviz as az @@ -694,6 +695,13 @@ def build_model( # type: ignore with self: self.add_coords(coords) + if vs_prior_type and ("mus" in priors or "sigmas" in priors): + warnings.warn( + "Variable selection priors specified. " + "The 'mus' and 'sigmas' in the priors dict will be ignored " + "for beta coefficients. Only 'eta' and 'lkj_sd' will be used." + ) + # Create coefficient priors if vs_prior_type: # Use variable selection priors diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb new file mode 100644 index 00000000..6823d382 --- /dev/null +++ b/docs/source/notebooks/iv_vs_priors.ipynb @@ -0,0 +1,1377 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 42, + "id": "532c6736", + "metadata": {}, + "outputs": [], + "source": [ + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import causalpy as cp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "046aa8e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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X.mean(axis=0)) / X.std(axis=0)\n", + "\n", + " mean = [0, 0]\n", + " cov = [[sigma_U**2, rho * sigma_U * sigma_V], [rho * sigma_U * sigma_V, sigma_V**2]]\n", + " errors = np.random.multivariate_normal(mean, cov, size=n)\n", + " U = errors[:, 0] # error in outcome equation\n", + " V = errors[:, 1] #\n", + "\n", + " # continuous treatment\n", + " T_cont = X @ betaD + V\n", + "\n", + " # latent variable for binary treatment\n", + " T_latent = X @ betaD + V\n", + " T_bin = np.random.binomial(n=1, p=inv_logit(T_latent), size=n)\n", + "\n", + " alpha_individual = 3.0 + 2.5 * X[:, 0]\n", + "\n", + " # outcomes\n", + " Y_cont = alpha_true * T_cont + X @ betaY + U\n", + " if cate_estimation:\n", + " Y_bin = alpha_individual * T_bin + X @ betaY + U\n", + " else:\n", + " Y_bin = alpha_true * T_bin + X @ betaY + U\n", + "\n", + " # combine into DataFrame\n", + " data = pd.DataFrame(\n", + " {\n", + " \"Y_cont\": Y_cont,\n", + " \"Y_bin\": Y_bin,\n", + " \"T_cont\": T_cont,\n", + " \"T_bin\": T_bin,\n", + " }\n", + " )\n", + " data[\"alpha\"] = alpha_true + alpha_individual\n", + " for j in range(p):\n", + " data[f\"feature_{j}\"] = X[:, j]\n", + " data[\"Y_cont_scaled\"] = (data[\"Y_cont\"] - data[\"Y_cont\"].mean()) / data[\n", + " \"Y_cont\"\n", + " ].std(ddof=1)\n", + " data[\"Y_bin_scaled\"] = (data[\"Y_bin\"] - data[\"Y_bin\"].mean()) / data[\"Y_bin\"].std(\n", + " ddof=1\n", + " )\n", + " data[\"T_cont_scaled\"] = (data[\"T_cont\"] - data[\"T_cont\"].mean()) / data[\n", + " \"T_cont\"\n", + " ].std(ddof=1)\n", + " data[\"T_bin_scaled\"] = (data[\"T_bin\"] - data[\"T_bin\"].mean()) / data[\"T_bin\"].std(\n", + " ddof=1\n", + " )\n", + " return data\n", + "\n", + "\n", + "data = simulate_data()\n", + "instruments_data = data.copy()\n", + "features = [col for col in data.columns if \"feature\" in col]\n", + "formula = \"Y_cont ~ T_cont + \" + \" + \".join(features)\n", + "instruments_formula = \"T_cont ~ 1 + \" + \" + \".join(features)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "763ca253", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--------------------------------------------------------------------------------\n", + "Model 1: Normal Priors (No Variable Selection)\n", + "--------------------------------------------------------------------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:172: UserWarning: Warning. The treatment variable is not Binary.\n", + " This is not necessarily a problem but it violates\n", + " the assumption of a simple IV experiment.\n", + " The coefficients should be interpreted appropriately.\n", + " \"\"\"Validate the input data and model formula for correctness\"\"\"\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (2 chains in 2 jobs)\n", + "NUTS: [beta_t, beta_z, chol_cov]\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n", + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "75ceaa4f49be4429bdb3671caae6d44f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n", + " outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n", + "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n", + " outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n" + ] + }, + { + "data": { + "text/html": [ + "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 69 seconds.\n",
+      "There were 17 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:172: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "                This is not necessarily a problem but it violates\n",
+      "                the assumption of a simple IV experiment.\n",
+      "                The coefficients should be interpreted appropriately.\n",
+      "  \"\"\"Validate the input data and model formula for correctness\"\"\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 2: Spike-and-Slab Priors\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "NUTS: [pi_beta_t, beta_t_raw, gamma_beta_t_u, pi_beta_z, beta_z_raw, gamma_beta_z_u, chol_cov]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8478b37ed9ea450c9d18f31ceacc9c16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 316 seconds.\n",
+      "There were 86 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample_kwargs = {\n",
+    "    \"draws\": 1000,\n",
+    "    \"tune\": 2000,\n",
+    "    \"chains\": 2,\n",
+    "    \"cores\": 2,\n",
+    "    \"mptarget_accept\": 0.95,\n",
+    "    \"progressbar\": True,\n",
+    "    \"random_seed\": 42,\n",
+    "    \"mp_ctx\": \"spawn\",\n",
+    "}\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 1: Normal priors (no selection)\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 1: Normal Priors (No Variable Selection)\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_normal = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=None,  # No variable selection\n",
+    ")\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 2: Spike-and-Slab priors\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 2: Spike-and-Slab Priors\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_spike_slab = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=\"spike_and_slab\",\n",
+    "    vs_hyperparams={\n",
+    "        \"pi_alpha\": 2,\n",
+    "        \"pi_beta\": 2,\n",
+    "        \"slab_sigma\": 2,\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "a9633162",
+   "metadata": {},
+   "outputs": [
+    {
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
beta_z[Intercept]0.0150.059-0.0980.1250.0010.0021658.01023.01.00
beta_z[T_cont]3.1540.3572.4293.7770.0290.011152.0373.01.01
beta_z[feature_0]0.4300.2330.0170.9050.0190.007159.0436.01.01
beta_z[feature_1]-0.3830.059-0.491-0.2770.0010.0021720.01250.01.00
beta_z[feature_2]0.0300.162-0.2720.3410.0120.005172.0418.01.01
beta_z[feature_3]-0.0550.143-0.3010.2420.0110.005171.0428.01.00
beta_z[feature_4]0.4420.0640.3350.5640.0030.004685.0668.01.00
beta_z[feature_5]-0.0000.058-0.1010.1130.0020.0021338.0970.01.00
beta_z[feature_6]0.0270.061-0.0940.1400.0020.0021184.01063.01.00
beta_z[feature_7]-0.0370.058-0.1400.0770.0020.001914.0675.01.00
beta_z[feature_8]-0.0760.060-0.1930.0340.0020.002827.0602.01.00
beta_z[feature_9]0.0420.060-0.0720.1460.0020.0041191.0809.01.00
beta_z[feature_10]-0.0340.061-0.1510.0770.0020.002776.0907.01.00
beta_z[feature_11]0.0750.059-0.0290.1900.0020.001864.0904.01.00
beta_z[feature_12]-0.0450.061-0.1600.0640.0020.002951.0946.01.00
beta_z[feature_13]-0.0400.059-0.1640.0600.0020.0021466.01266.01.00
\n",
+       "
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+      ],
+      "text/plain": [
+       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "beta_z[Intercept]   0.015  0.059  -0.098    0.125      0.001    0.002   \n",
+       "beta_z[T_cont]      3.154  0.357   2.429    3.777      0.029    0.011   \n",
+       "beta_z[feature_0]   0.430  0.233   0.017    0.905      0.019    0.007   \n",
+       "beta_z[feature_1]  -0.383  0.059  -0.491   -0.277      0.001    0.002   \n",
+       "beta_z[feature_2]   0.030  0.162  -0.272    0.341      0.012    0.005   \n",
+       "beta_z[feature_3]  -0.055  0.143  -0.301    0.242      0.011    0.005   \n",
+       "beta_z[feature_4]   0.442  0.064   0.335    0.564      0.003    0.004   \n",
+       "beta_z[feature_5]  -0.000  0.058  -0.101    0.113      0.002    0.002   \n",
+       "beta_z[feature_6]   0.027  0.061  -0.094    0.140      0.002    0.002   \n",
+       "beta_z[feature_7]  -0.037  0.058  -0.140    0.077      0.002    0.001   \n",
+       "beta_z[feature_8]  -0.076  0.060  -0.193    0.034      0.002    0.002   \n",
+       "beta_z[feature_9]   0.042  0.060  -0.072    0.146      0.002    0.004   \n",
+       "beta_z[feature_10] -0.034  0.061  -0.151    0.077      0.002    0.002   \n",
+       "beta_z[feature_11]  0.075  0.059  -0.029    0.190      0.002    0.001   \n",
+       "beta_z[feature_12] -0.045  0.061  -0.160    0.064      0.002    0.002   \n",
+       "beta_z[feature_13] -0.040  0.059  -0.164    0.060      0.002    0.002   \n",
+       "\n",
+       "                    ess_bulk  ess_tail  r_hat  \n",
+       "beta_z[Intercept]     1658.0    1023.0   1.00  \n",
+       "beta_z[T_cont]         152.0     373.0   1.01  \n",
+       "beta_z[feature_0]      159.0     436.0   1.01  \n",
+       "beta_z[feature_1]     1720.0    1250.0   1.00  \n",
+       "beta_z[feature_2]      172.0     418.0   1.01  \n",
+       "beta_z[feature_3]      171.0     428.0   1.00  \n",
+       "beta_z[feature_4]      685.0     668.0   1.00  \n",
+       "beta_z[feature_5]     1338.0     970.0   1.00  \n",
+       "beta_z[feature_6]     1184.0    1063.0   1.00  \n",
+       "beta_z[feature_7]      914.0     675.0   1.00  \n",
+       "beta_z[feature_8]      827.0     602.0   1.00  \n",
+       "beta_z[feature_9]     1191.0     809.0   1.00  \n",
+       "beta_z[feature_10]     776.0     907.0   1.00  \n",
+       "beta_z[feature_11]     864.0     904.0   1.00  \n",
+       "beta_z[feature_12]     951.0     946.0   1.00  \n",
+       "beta_z[feature_13]    1466.0    1266.0   1.00  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(result_normal.idata, var_names=[\"beta_z\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "bfcdeecd",
+   "metadata": {},
+   "outputs": [
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
beta_z[Intercept]0.0010.010-0.0150.0110.0000.0011035.0807.01.00
beta_z[T_cont]3.0760.0992.9003.2690.0060.004286.0340.01.00
beta_z[feature_0]0.4760.0860.3230.6480.0040.002477.0571.01.00
beta_z[feature_1]-0.3920.047-0.480-0.3060.0010.0011903.01459.01.00
beta_z[feature_2]-0.0000.018-0.0230.0190.0010.002464.0886.01.01
beta_z[feature_3]-0.0030.021-0.0310.0180.0010.003357.0313.01.00
beta_z[feature_4]0.4230.0490.3410.5200.0030.001380.0176.01.01
beta_z[feature_5]0.0000.009-0.0120.0080.0000.001629.01103.01.00
beta_z[feature_6]0.0030.016-0.0120.0330.0000.001781.01101.01.00
beta_z[feature_7]-0.0050.019-0.0380.0150.0010.001738.0472.01.01
beta_z[feature_8]-0.0040.018-0.0390.0120.0010.001700.0973.01.00
beta_z[feature_9]0.0020.014-0.0120.0130.0000.001925.01187.01.00
beta_z[feature_10]-0.0010.010-0.0110.0120.0000.0011407.01059.01.00
beta_z[feature_11]0.0040.019-0.0160.0310.0010.001787.0727.01.01
beta_z[feature_12]-0.0060.023-0.0500.0100.0010.001394.0684.01.01
beta_z[feature_13]-0.0010.011-0.0120.0050.0000.001746.01345.01.00
\n",
+       "
"
+      ],
+      "text/plain": [
+       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
+       "beta_z[Intercept]   0.001  0.010  -0.015    0.011      0.000    0.001   \n",
+       "beta_z[T_cont]      3.076  0.099   2.900    3.269      0.006    0.004   \n",
+       "beta_z[feature_0]   0.476  0.086   0.323    0.648      0.004    0.002   \n",
+       "beta_z[feature_1]  -0.392  0.047  -0.480   -0.306      0.001    0.001   \n",
+       "beta_z[feature_2]  -0.000  0.018  -0.023    0.019      0.001    0.002   \n",
+       "beta_z[feature_3]  -0.003  0.021  -0.031    0.018      0.001    0.003   \n",
+       "beta_z[feature_4]   0.423  0.049   0.341    0.520      0.003    0.001   \n",
+       "beta_z[feature_5]   0.000  0.009  -0.012    0.008      0.000    0.001   \n",
+       "beta_z[feature_6]   0.003  0.016  -0.012    0.033      0.000    0.001   \n",
+       "beta_z[feature_7]  -0.005  0.019  -0.038    0.015      0.001    0.001   \n",
+       "beta_z[feature_8]  -0.004  0.018  -0.039    0.012      0.001    0.001   \n",
+       "beta_z[feature_9]   0.002  0.014  -0.012    0.013      0.000    0.001   \n",
+       "beta_z[feature_10] -0.001  0.010  -0.011    0.012      0.000    0.001   \n",
+       "beta_z[feature_11]  0.004  0.019  -0.016    0.031      0.001    0.001   \n",
+       "beta_z[feature_12] -0.006  0.023  -0.050    0.010      0.001    0.001   \n",
+       "beta_z[feature_13] -0.001  0.011  -0.012    0.005      0.000    0.001   \n",
+       "\n",
+       "                    ess_bulk  ess_tail  r_hat  \n",
+       "beta_z[Intercept]     1035.0     807.0   1.00  \n",
+       "beta_z[T_cont]         286.0     340.0   1.00  \n",
+       "beta_z[feature_0]      477.0     571.0   1.00  \n",
+       "beta_z[feature_1]     1903.0    1459.0   1.00  \n",
+       "beta_z[feature_2]      464.0     886.0   1.01  \n",
+       "beta_z[feature_3]      357.0     313.0   1.00  \n",
+       "beta_z[feature_4]      380.0     176.0   1.01  \n",
+       "beta_z[feature_5]      629.0    1103.0   1.00  \n",
+       "beta_z[feature_6]      781.0    1101.0   1.00  \n",
+       "beta_z[feature_7]      738.0     472.0   1.01  \n",
+       "beta_z[feature_8]      700.0     973.0   1.00  \n",
+       "beta_z[feature_9]      925.0    1187.0   1.00  \n",
+       "beta_z[feature_10]    1407.0    1059.0   1.00  \n",
+       "beta_z[feature_11]     787.0     727.0   1.01  \n",
+       "beta_z[feature_12]     394.0     684.0   1.01  \n",
+       "beta_z[feature_13]     746.0    1345.0   1.00  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(result_spike_slab.idata, var_names=[\"beta_z\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "838e0726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_spike_slab.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "127888b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([], dtype=object)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09b24bf",
+   "metadata": {},
+   "source": [
+    "## The Treatment Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "acafc928",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([], dtype=object)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_t\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "CausalPy",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From db0522e5dc4ce5f5fe2f703bd1036e8ee54ecb37 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 21:04:09 +0000
Subject: [PATCH 03/17] trying to fix doctests

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 38 ++++++++++-----------------
 1 file changed, 14 insertions(+), 24 deletions(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index c4b04622..d13ad8f8 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -41,14 +41,14 @@ def _relaxed_bernoulli_transform(
     Parameters
     ----------
     p : float or PyMC variable
-        Probability parameter
+        Probability parameter.
     temperature : float, default=0.1
-        Temperature parameter (lower = more binary)
+        Temperature parameter (lower = more binary).
 
     Returns
     -------
     function
-        Transform function that takes uniform random variable
+        Transform function that takes uniform random variable.
     """
 
     def transform(u):
@@ -84,6 +84,8 @@ class SpikeAndSlabPrior:
 
     Example
     -------
+    >>> import pymc as pm
+    >>> from causalpy.variable_selection_priors import SpikeAndSlabPrior
     >>> spike_slab = SpikeAndSlabPrior(dims="features")
     >>> with pm.Model():
     ...     beta = spike_slab.create_variable("beta")
@@ -161,6 +163,8 @@ class HorseshoePrior:
 
     Example
     -------
+    >>> import pymc as pm
+    >>> from causalpy.variable_selection_priors import HorseshoePrior
     >>> horseshoe = HorseshoePrior(dims="features")
     >>> with pm.Model():
     ...     beta = horseshoe.create_variable("beta")
@@ -261,18 +265,11 @@ class VariableSelectionPrior:
     -------
     >>> import pymc as pm
     >>> from variable_selection_priors import VariableSelectionPrior
-    >>>
     >>> # Create spike-and-slab prior
     >>> vs_prior = VariableSelectionPrior("spike_and_slab")
-    >>>
     >>> with pm.Model() as model:
     ...     # Create coefficients with variable selection
-    ...     beta = vs_prior.create_prior(
-    ...         name="beta",
-    ...         n_params=5,
-    ...         dims="features",
-    ...         X=X_train,  # For computing tau0 in horseshoe
-    ...     )
+    ...     beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
     """
 
     def __init__(self, prior_type: str, hyperparams: Optional[Dict[str, Any]] = None):
@@ -375,11 +372,13 @@ def create_prior(
 
         Example
         -------
-        >>> vs_prior = VariableSelectionPrior("horseshoe")
+        >>> import pymc as pm
+        >>> import pandas as pd
+        >>> from variable_selection_priors import VariableSelectionPrior
+        >>> X_train = pd.DataFrame{'x': [1, 2, 3, 4]}
+        >>> vs_prior = VariableSelectionPrior("spike_and_slab")
         >>> with pm.Model() as model:
-        ...     beta = vs_prior.create_prior(
-        ...         "beta", n_params=10, dims="features", X=X_train
-        ...     )
+        ...     beta = vs_prior.create_prior("beta", n_params=4, dims="features")
         """
         # Merge instance and call-specific hyperparameters
         default_hp = self._get_default_hyperparams(n_params, X)
@@ -450,11 +449,6 @@ def get_inclusion_probabilities(
         ValueError
             If prior_type is not 'spike_and_slab' or gamma variables not found
 
-        Example
-        -------
-        >>> result = vs_prior.get_inclusion_probabilities(idata, "beta")
-        >>> print(f"Selected features: {result['selected']}")
-        >>> print(f"Inclusion probs: {result['probabilities']}")
         """
         if self.prior_type != "spike_and_slab":
             raise ValueError(
@@ -512,10 +506,6 @@ def get_shrinkage_factors(self, idata, param_name: str) -> Dict[str, np.ndarray]
         ValueError
             If prior_type is not 'horseshoe' or required variables not found
 
-        Example
-        -------
-        >>> result = vs_prior.get_shrinkage_factors(idata, "beta")
-        >>> print(f"Shrinkage factors: {result['shrinkage_factors']}")
         """
         if self.prior_type != "horseshoe":
             raise ValueError("Shrinkage factors only available for 'horseshoe' priors")

From 1670af48836fd379a627847ea633124c8696ce0d Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 21:11:15 +0000
Subject: [PATCH 04/17] adding fix

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index d13ad8f8..f5e6e5b8 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -375,7 +375,6 @@ def create_prior(
         >>> import pymc as pm
         >>> import pandas as pd
         >>> from variable_selection_priors import VariableSelectionPrior
-        >>> X_train = pd.DataFrame{'x': [1, 2, 3, 4]}
         >>> vs_prior = VariableSelectionPrior("spike_and_slab")
         >>> with pm.Model() as model:
         ...     beta = vs_prior.create_prior("beta", n_params=4, dims="features")
@@ -568,14 +567,6 @@ def create_variable_selection_prior(
     PyMC variable
         The coefficient vector with specified prior
 
-    Example
-    -------
-    >>> with pm.Model() as model:
-    ...     X = pm.Data("X", X_train)
-    ...     beta = create_variable_selection_prior(
-    ...         "spike_and_slab", "beta", n_params=X_train.shape[1], dims="features"
-    ...     )
-    ...     mu = pm.math.dot(X, beta)
     """
     vs_prior = VariableSelectionPrior(prior_type, hyperparams)
     return vs_prior.create_prior(name, n_params, dims, X)

From 83061e42840d53a89b9bea537d5c87039c171ce3 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 21:18:47 +0000
Subject: [PATCH 05/17] another fix

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index f5e6e5b8..4f8a39a3 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -87,7 +87,8 @@ class SpikeAndSlabPrior:
     >>> import pymc as pm
     >>> from causalpy.variable_selection_priors import SpikeAndSlabPrior
     >>> spike_slab = SpikeAndSlabPrior(dims="features")
-    >>> with pm.Model():
+    >>> coords = {"features": ["a", "b", "c", "d", "e"]}
+    >>> with pm.Model(coords=coords) as model:
     ...     beta = spike_slab.create_variable("beta")
     """
 
@@ -166,7 +167,8 @@ class HorseshoePrior:
     >>> import pymc as pm
     >>> from causalpy.variable_selection_priors import HorseshoePrior
     >>> horseshoe = HorseshoePrior(dims="features")
-    >>> with pm.Model():
+    >>> coords = {"features": ["a", "b", "c", "d", "e"]}
+    >>> with pm.Model(coords=coords) as model:
     ...     beta = horseshoe.create_variable("beta")
     """
 
@@ -264,10 +266,11 @@ class VariableSelectionPrior:
     Example
     -------
     >>> import pymc as pm
-    >>> from variable_selection_priors import VariableSelectionPrior
+    >>> from causalpy.variable_selection_priors import VariableSelectionPrior
     >>> # Create spike-and-slab prior
     >>> vs_prior = VariableSelectionPrior("spike_and_slab")
-    >>> with pm.Model() as model:
+    >>> coords = {"features": ["a", "b", "c", "d", "e"]}
+    >>> with pm.Model(coords=coords) as model:
     ...     # Create coefficients with variable selection
     ...     beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
     """
@@ -374,7 +377,7 @@ def create_prior(
         -------
         >>> import pymc as pm
         >>> import pandas as pd
-        >>> from variable_selection_priors import VariableSelectionPrior
+        >>> from causalpy.variable_selection_priors import VariableSelectionPrior
         >>> vs_prior = VariableSelectionPrior("spike_and_slab")
         >>> with pm.Model() as model:
         ...     beta = vs_prior.create_prior("beta", n_params=4, dims="features")

From 05dc20d5df29490ff9f30843cd845caad8d71bf2 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 22:07:47 +0000
Subject: [PATCH 06/17] update fix

Signed-off-by: Nathaniel 
---
 causalpy/variable_selection_priors.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index 4f8a39a3..fdac7ff4 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -379,7 +379,8 @@ def create_prior(
         >>> import pandas as pd
         >>> from causalpy.variable_selection_priors import VariableSelectionPrior
         >>> vs_prior = VariableSelectionPrior("spike_and_slab")
-        >>> with pm.Model() as model:
+        >>> coords = {"features": ["a", "b", "c", "d", "e"]}
+        >>> with pm.Model(coords=coords) as model:
         ...     beta = vs_prior.create_prior("beta", n_params=4, dims="features")
         """
         # Merge instance and call-specific hyperparameters

From 73e6a8dc958659a90e496690dc81bd1567b1cbcd Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Thu, 20 Nov 2025 22:41:10 +0000
Subject: [PATCH 07/17] adding tests

Signed-off-by: Nathaniel 
---
 causalpy/experiments/instrumental_variable.py |   4 +-
 .../tests/test_variable_selection_priors.py   | 114 ++++++++++++++++++
 docs/source/_static/interrogate_badge.svg     |   6 +-
 3 files changed, 119 insertions(+), 5 deletions(-)
 create mode 100644 causalpy/tests/test_variable_selection_priors.py

diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py
index 67852123..5b6b4128 100644
--- a/causalpy/experiments/instrumental_variable.py
+++ b/causalpy/experiments/instrumental_variable.py
@@ -51,10 +51,10 @@ class InstrumentalVariable(BaseExperiment):
         If priors are not specified we will substitute MLE estimates for
         the beta coefficients. Example: ``priors = {"mus": [0, 0],
         "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}``.
-    :param vs_prior_type : str or None, default=None
+    vs_prior_type : str or None, default=None
         Type of variable selection prior: 'spike_and_slab', 'horseshoe', or None.
         If None, uses standard normal priors.
-    :param vs_hyperparams : dict, optional
+    vs_hyperparams : dict, optional
         Hyperparameters for variable selection priors. Only used if vs_prior_type
         is not None.
 
diff --git a/causalpy/tests/test_variable_selection_priors.py b/causalpy/tests/test_variable_selection_priors.py
new file mode 100644
index 00000000..09de5885
--- /dev/null
+++ b/causalpy/tests/test_variable_selection_priors.py
@@ -0,0 +1,114 @@
+#   Copyright 2022 - 2025 The PyMC Labs Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+import numpy as np
+import pymc as pm
+import pytest
+
+from causalpy.variable_selection_priors import (
+    HorseshoePrior,
+    SpikeAndSlabPrior,
+    VariableSelectionPrior,
+    create_variable_selection_prior,
+)
+
+
+@pytest.fixture
+def sample_data():
+    """Generate sample design matrix for testing."""
+    rng = np.random.default_rng(42)
+    n_obs = 100
+    n_features = 5
+    X = rng.normal(size=(n_obs, n_features))
+    return X
+
+
+@pytest.fixture
+def coords():
+    """Generate sample coordinates for PyMC models."""
+    return {"features": [f"x_{i}" for i in range(5)]}
+
+
+def test_create_variable_in_model_context(coords):
+    """Test that create_variable works in PyMC model context."""
+    prior = SpikeAndSlabPrior(dims="features")
+
+    with pm.Model(coords=coords) as model:
+        beta = prior.create_variable("beta")
+
+        # Check that beta was created
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+        # Check that intermediate variables were created
+        assert "pi_beta" in model.named_vars
+        assert "beta_raw" in model.named_vars
+        assert "gamma_beta" in model.named_vars
+
+
+def test_create_variable_in_model_context_horseshoe(coords):
+    """Test that create_variable works in PyMC model context."""
+    prior = HorseshoePrior(dims="features")
+
+    with pm.Model(coords=coords) as model:
+        beta = prior.create_variable("beta")
+
+        # Check that beta was created
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+        # Check that intermediate variables were created
+        assert "tau_beta" in model.named_vars
+        assert "lambda_beta" in model.named_vars
+        assert "c2_beta" in model.named_vars
+        assert "lambda_tilde_beta" in model.named_vars
+        assert "beta_raw" in model.named_vars
+
+
+def test_create_prior_spike_and_slab(coords):
+    """Test create_prior for spike-and-slab."""
+    vs_prior = VariableSelectionPrior("spike_and_slab")
+
+    with pm.Model(coords=coords) as model:
+        beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
+
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+
+def test_create_prior_horseshoe(coords, sample_data):
+    """Test create_prior for horseshoe."""
+    vs_prior = VariableSelectionPrior("horseshoe")
+
+    with pm.Model(coords=coords) as model:
+        beta = vs_prior.create_prior(
+            name="beta", n_params=5, dims="features", X=sample_data
+        )
+
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+
+def test_convenience_function_with_custom_hyperparams(coords):
+    """Test convenience function with custom hyperparameters."""
+    with pm.Model(coords=coords) as model:
+        _ = create_variable_selection_prior(
+            prior_type="spike_and_slab",
+            name="beta",
+            n_params=5,
+            dims="features",
+            hyperparams={"slab_sigma": 5},
+        )
+
+        assert "beta" in model.named_vars
diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg
index d2d886ad..a625f245 100644
--- a/docs/source/_static/interrogate_badge.svg
+++ b/docs/source/_static/interrogate_badge.svg
@@ -1,5 +1,5 @@
 
-    interrogate: 95.4%
+    interrogate: 95.2%
     
         
             
@@ -12,8 +12,8 @@
     
         interrogate
         interrogate
-        95.4%
-        95.4%
+        95.2%
+        95.2%
     
     
         

From 8d6251fdc092c3ad70ebf869c40926b2ede69ad0 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 09:21:02 +0000
Subject: [PATCH 08/17] update adding more tests

Signed-off-by: Nathaniel 
---
 causalpy/pymc_models.py                       |   16 +-
 .../tests/test_integration_pymc_examples.py   |   39 +
 causalpy/variable_selection_priors.py         |   56 +-
 docs/source/_static/interrogate_badge.svg     |    8 +-
 docs/source/notebooks/iv_vs_priors.ipynb      | 1467 ++++++++---------
 5 files changed, 798 insertions(+), 788 deletions(-)

diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index d0c04bfd..7d30dd5a 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -684,9 +684,9 @@ def build_model(  # type: ignore
             Dictionary of priors for the mus and sigmas of both
             regressions. Example: ``priors = {"mus": [0, 0],
             "sigmas": [1, 1], "eta": 2, "lkj_sd": 2}``.
-        :param vs_prior_type: An optional string. Can be "spike_and_slab"
+        vs_prior_type: An optional string. Can be "spike_and_slab"
                               or "horseshoe" or "normal
-        :param vs_hyperparams: An optional dictionary of priors for the
+        vs_hyperparams: An optional dictionary of priors for the
                                variable selection hyperparameters
 
         """
@@ -705,16 +705,18 @@ def build_model(  # type: ignore
             # Create coefficient priors
             if vs_prior_type:
                 # Use variable selection priors
-                vs_prior_treatment = VariableSelectionPrior(
+                self.vs_prior_treatment = VariableSelectionPrior(
+                    vs_prior_type, vs_hyperparams
+                )
+                self.vs_prior_outcome = VariableSelectionPrior(
                     vs_prior_type, vs_hyperparams
                 )
-                vs_prior_outcome = VariableSelectionPrior(vs_prior_type, vs_hyperparams)
 
-                beta_t = vs_prior_treatment.create_prior(
+                beta_t = self.vs_prior_treatment.create_prior(
                     name="beta_t", n_params=Z.shape[1], dims="instruments", X=Z
                 )
 
-                beta_z = vs_prior_outcome.create_prior(
+                beta_z = self.vs_prior_outcome.create_prior(
                     name="beta_z", n_params=X.shape[1], dims="covariates", X=X
                 )
             else:
@@ -733,7 +735,7 @@ def build_model(  # type: ignore
                 )
 
             sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2)
-            chol, corr, sigmas = pm.LKJCholeskyCov(
+            chol, _, _ = pm.LKJCholeskyCov(
                 name="chol_cov",
                 eta=priors["eta"],
                 n=2,
diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py
index e3a1a1b9..efeb6bf9 100644
--- a/causalpy/tests/test_integration_pymc_examples.py
+++ b/causalpy/tests/test_integration_pymc_examples.py
@@ -706,6 +706,45 @@ def test_iv_reg_vs_prior(mock_pymc_sample):
         result.get_plot_data()
     assert "gamma_beta_t" in result.model.named_vars
     assert "pi_beta_t" in result.model.named_vars
+    summary = result.model.vs_prior_outcome.get_inclusion_probabilities(
+        result.idata, "beta_z"
+    )
+    assert isinstance(summary, pd.DataFrame)
+
+
+@pytest.mark.integration
+def test_iv_reg_vs_prior_hs(mock_pymc_sample):
+    df = cp.load_data("risk")
+    instruments_formula = "risk  ~ 1 + logmort0"
+    formula = "loggdp ~  1 + risk"
+    instruments_data = df[["risk", "logmort0"]]
+    data = df[["loggdp", "risk"]]
+
+    result = cp.InstrumentalVariable(
+        instruments_data=instruments_data,
+        data=data,
+        instruments_formula=instruments_formula,
+        formula=formula,
+        model=cp.pymc_models.InstrumentalVariableRegression(
+            sample_kwargs=sample_kwargs
+        ),
+        vs_prior_type="horseshoe",
+    )
+    result.model.sample_predictive_distribution(ppc_sampler="pymc")
+    assert isinstance(df, pd.DataFrame)
+    assert isinstance(data, pd.DataFrame)
+    assert isinstance(instruments_data, pd.DataFrame)
+    assert isinstance(result, cp.InstrumentalVariable)
+    assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"]
+    assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"]
+    with pytest.raises(NotImplementedError):
+        result.get_plot_data()
+    assert "tau_beta_t" in result.model.named_vars
+    assert "tau_beta_z" in result.model.named_vars
+    summary = result.model.vs_prior_outcome.get_shrinkage_factors(
+        result.idata, "beta_z"
+    )
+    assert isinstance(summary, pd.DataFrame)
 
 
 @pytest.mark.integration
diff --git a/causalpy/variable_selection_priors.py b/causalpy/variable_selection_priors.py
index fdac7ff4..8c8b2001 100644
--- a/causalpy/variable_selection_priors.py
+++ b/causalpy/variable_selection_priors.py
@@ -23,6 +23,7 @@
 from typing import Any, Dict, Optional, Union
 
 import numpy as np
+import pandas as pd
 import pymc as pm
 import pytensor.tensor as pt
 from pymc_extras.prior import Prior
@@ -65,9 +66,10 @@ class SpikeAndSlabPrior:
     Creates a mixture prior with a point mass at zero (spike) and a diffuse
     normal distribution (slab), implemented as:
 
-    β_j = γ_j × β_j^raw
-
-    where γ_j ∈ [0,1] is a relaxed indicator and β_j^raw ~ N(0, σ_slab²).
+    .. math::
+        \beta_{j} = \gamma_{j} \cdot \beta_{j}^{\text{raw}} \\
+        \beta_{j}^{\text{raw}} \sim \mathcal{N}(0, \sigma_{\text{slab}}^{2}), \qquad
+        \gamma_{j} \in [0,1].
 
     Parameters
     ----------
@@ -145,9 +147,9 @@ class HorseshoePrior:
     Provides continuous shrinkage with heavy tails, allowing strong signals
     to escape shrinkage while weak signals are dampened:
 
-    β_j = τ · λ̃_j · β_j^raw
-
-    where λ̃_j = √(c²λ_j² / (c² + τ²λ_j²)) is the regularized local shrinkage.
+    .. math::
+        \beta_{j} & =  \tau \cdot \lambda_{j} \cdot \beta_{j}^{raw}  \\
+        \lambda_{j} & = \sqrt{ \dfrac{c^{2}\lambda_{j}^{2}}{c^{2} + \tau^{2}\lambda_{j}^{2}} }
 
     Parameters
     ----------
@@ -423,7 +425,7 @@ def create_prior(
 
     def get_inclusion_probabilities(
         self, idata, param_name: str, threshold: float = 0.5
-    ) -> Dict[str, np.ndarray]:
+    ) -> pd.DataFrame:
         """
         Extract variable inclusion probabilities from fitted model.
 
@@ -472,17 +474,24 @@ def get_inclusion_probabilities(
         gamma = az.extract(idata.posterior[gamma_name])
 
         # Compute inclusion probabilities
-        probabilities = (gamma > threshold).mean(dim="sample").values
-        gamma_mean = gamma.mean(dim="sample").values
+        probabilities = (gamma > threshold).mean(dim="sample").to_array()
+        gamma_mean = gamma.mean(dim="sample").to_array()
         selected = probabilities > threshold
 
-        return {
+        summary = {
             "probabilities": probabilities,
             "selected": selected,
             "gamma_mean": gamma_mean,
         }
+        probs = summary["probabilities"].T
+        df = pd.DataFrame(index=list(range(len(probs))))
+
+        df["prob"] = probs
+        df["selected"] = summary["selected"].T
+        df["gamma_mean"] = summary["gamma_mean"].T
+        return df
 
-    def get_shrinkage_factors(self, idata, param_name: str) -> Dict[str, np.ndarray]:
+    def get_shrinkage_factors(self, idata, param_name: str) -> pd.DataFrame:
         """
         Extract shrinkage factors from horseshoe prior.
 
@@ -524,17 +533,26 @@ def get_shrinkage_factors(self, idata, param_name: str) -> Dict[str, np.ndarray]
             raise ValueError(f"Could not find '{lambda_tilde_name}' in posterior")
 
         # Extract components
-        tau = az.extract(idata.posterior[tau_name])
-        lambda_tilde = az.extract(idata.posterior[lambda_tilde_name])
+        tau = az.extract(idata.posterior[tau_name]).to_array()
+        lambda_tilde = az.extract(idata.posterior[lambda_tilde_name]).to_array()
 
-        # Compute shrinkage factors
-        shrinkage_factors = (tau * lambda_tilde).mean(dim="sample").values
+        shrinkage_factor = np.array(
+            [tau[0, i] * lambda_tilde[0, :, :] for i in range(len(tau))]
+        )
+        shrinkage_factor = shrinkage_factor.mean(axis=2)
 
-        return {
-            "shrinkage_factors": shrinkage_factors,
-            "tau": tau.mean().values,
-            "lambda_tilde": lambda_tilde.mean(dim="sample").values,
+        summary = {
+            "shrinkage_factors": shrinkage_factor,
+            "tau": tau.mean(),
+            "lambda_tilde": lambda_tilde.mean(dim=("sample")),
         }
+        probs = summary["shrinkage_factors"].T
+        df = pd.DataFrame(index=list(range(len(probs))))
+        df["shrinkage_factor"] = probs
+
+        df["lambda_tilde"] = summary["lambda_tilde"].T
+        df["tau"] = np.mean(tau).item()
+        return df
 
 
 def create_variable_selection_prior(
diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg
index a625f245..f135fc49 100644
--- a/docs/source/_static/interrogate_badge.svg
+++ b/docs/source/_static/interrogate_badge.svg
@@ -1,10 +1,10 @@
 
-    interrogate: 95.2%
+    interrogate: 95.0%
     
         
             
         
-        
+        
         
             
         
@@ -12,8 +12,8 @@
     
         interrogate
         interrogate
-        95.2%
-        95.2%
+        95.0%
+        95.0%
     
     
         
diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 6823d382..97e221ad 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -2,22 +2,35 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 129,
    "id": "532c6736",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import pymc as pm\n",
+    "\n",
+    "import causalpy as cp\n",
     "\n",
-    "import causalpy as cp"
+    "%load_ext autoreload\n",
+    "%autoreload 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "046aa8e0",
    "metadata": {},
    "outputs": [
@@ -68,123 +81,123 @@
        "  \n",
        "    
\n",
        "      0\n",
-       "      -3.622930\n",
-       "      -4.426916\n",
-       "      0.267995\n",
-       "      0\n",
-       "      7.231021\n",
-       "      0.492408\n",
-       "      -0.153387\n",
-       "      0.632238\n",
-       "      1.549490\n",
-       "      -0.217191\n",
+       "      17.285021\n",
+       "      5.560022\n",
+       "      4.908333\n",
+       "      1\n",
+       "      4.221970\n",
+       "      -0.711212\n",
+       "      0.678770\n",
+       "      0.807216\n",
+       "      1.502488\n",
+       "      -0.194990\n",
        "      ...\n",
-       "      -0.483356\n",
-       "      0.541115\n",
-       "      -0.438765\n",
-       "      -0.419556\n",
-       "      0.237334\n",
-       "      -1.976732\n",
-       "      -0.320051\n",
-       "      -1.506112\n",
-       "      0.089369\n",
-       "      -0.985505\n",
+       "      -0.355420\n",
+       "      1.402186\n",
+       "      -1.517787\n",
+       "      0.292296\n",
+       "      -0.439312\n",
+       "      -0.400863\n",
+       "      1.478663\n",
+       "      1.030321\n",
+       "      1.535974\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      1\n",
-       "      -7.762899\n",
-       "      -1.634123\n",
-       "      -2.042926\n",
+       "      -5.418737\n",
+       "      -1.169369\n",
+       "      -1.416456\n",
        "      0\n",
-       "      1.650064\n",
-       "      -1.739974\n",
-       "      -0.574608\n",
-       "      -1.009005\n",
-       "      0.327720\n",
-       "      -0.879195\n",
+       "      3.571828\n",
+       "      -0.971269\n",
+       "      0.155694\n",
+       "      0.094208\n",
+       "      1.060349\n",
+       "      0.918542\n",
        "      ...\n",
-       "      0.055404\n",
-       "      -1.411090\n",
-       "      -0.518275\n",
-       "      0.152670\n",
-       "      -1.120139\n",
-       "      0.360184\n",
-       "      -0.686483\n",
-       "      -0.795379\n",
-       "      -0.662693\n",
-       "      -0.985505\n",
+       "      0.647043\n",
+       "      0.978512\n",
+       "      0.102692\n",
+       "      -1.338446\n",
+       "      1.164631\n",
+       "      1.157542\n",
+       "      -0.477164\n",
+       "      -0.677874\n",
+       "      -0.459080\n",
+       "      -0.998202\n",
        "    
\n",
        "    
\n",
        "      2\n",
-       "      11.977784\n",
-       "      5.799723\n",
-       "      3.059354\n",
+       "      8.329916\n",
+       "      5.750195\n",
+       "      1.859907\n",
        "      1\n",
-       "      4.474363\n",
-       "      -0.610255\n",
-       "      -0.305802\n",
-       "      -0.602653\n",
-       "      1.882276\n",
-       "      -0.000420\n",
+       "      6.389524\n",
+       "      0.155810\n",
+       "      0.097990\n",
+       "      1.886364\n",
+       "      0.204515\n",
+       "      1.407368\n",
        "      ...\n",
-       "      0.197202\n",
-       "      -1.941906\n",
-       "      -1.287992\n",
-       "      0.237949\n",
-       "      0.721191\n",
-       "      0.151575\n",
-       "      1.060779\n",
-       "      1.096449\n",
-       "      0.997784\n",
-       "      1.014302\n",
+       "      -0.789757\n",
+       "      -1.376844\n",
+       "      -1.164524\n",
+       "      -0.854488\n",
+       "      -0.563027\n",
+       "      -1.762406\n",
+       "      0.707221\n",
+       "      1.078595\n",
+       "      0.574396\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      3\n",
-       "      -5.484061\n",
-       "      1.275083\n",
-       "      -2.253048\n",
+       "      -14.109005\n",
+       "      -3.260798\n",
+       "      -3.616069\n",
        "      0\n",
-       "      5.692706\n",
-       "      -0.122917\n",
-       "      -0.315150\n",
-       "      -1.469290\n",
-       "      -0.717482\n",
-       "      -0.439688\n",
+       "      1.522008\n",
+       "      -1.791197\n",
+       "      -0.025364\n",
+       "      -1.849614\n",
+       "      -0.420513\n",
+       "      -0.813558\n",
        "      ...\n",
-       "      0.312800\n",
-       "      -0.379406\n",
-       "      -0.648433\n",
-       "      0.649580\n",
-       "      1.006272\n",
-       "      0.927402\n",
-       "      -0.484782\n",
-       "      -0.055020\n",
-       "      -0.731075\n",
-       "      -0.985505\n",
+       "      -0.967529\n",
+       "      0.604905\n",
+       "      0.136397\n",
+       "      0.087200\n",
+       "      -0.575056\n",
+       "      -0.244472\n",
+       "      -1.225791\n",
+       "      -1.208765\n",
+       "      -1.152913\n",
+       "      -0.998202\n",
        "    
\n",
        "    
\n",
        "      4\n",
-       "      -12.907767\n",
-       "      -2.415022\n",
-       "      -3.497581\n",
-       "      0\n",
-       "      3.875030\n",
-       "      -0.849988\n",
-       "      -0.323205\n",
-       "      0.319486\n",
-       "      0.996123\n",
-       "      -0.457897\n",
+       "      15.927794\n",
+       "      6.070905\n",
+       "      4.285630\n",
+       "      1\n",
+       "      8.262360\n",
+       "      0.904944\n",
+       "      -0.648125\n",
+       "      -0.420076\n",
+       "      0.774960\n",
+       "      0.612525\n",
        "      ...\n",
-       "      0.802840\n",
-       "      1.348549\n",
-       "      -0.054392\n",
-       "      1.038429\n",
-       "      0.353959\n",
-       "      -0.682012\n",
-       "      -1.141859\n",
-       "      -0.994109\n",
-       "      -1.136093\n",
-       "      -0.985505\n",
+       "      2.362694\n",
+       "      2.702110\n",
+       "      -1.565191\n",
+       "      1.689404\n",
+       "      1.198610\n",
+       "      0.117392\n",
+       "      1.361744\n",
+       "      1.160004\n",
+       "      1.339552\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      ...\n",
@@ -212,123 +225,123 @@
        "    
\n",
        "    
\n",
        "      2495\n",
-       "      -10.076508\n",
-       "      -5.832192\n",
-       "      -1.414772\n",
-       "      0\n",
-       "      9.270431\n",
-       "      1.308172\n",
-       "      -0.802954\n",
-       "      -2.487989\n",
-       "      1.100350\n",
-       "      -1.214653\n",
+       "      -6.973181\n",
+       "      -2.492475\n",
+       "      -0.493568\n",
+       "      1\n",
+       "      6.399022\n",
+       "      0.159609\n",
+       "      2.004918\n",
+       "      1.850936\n",
+       "      -0.205821\n",
+       "      1.826866\n",
        "      ...\n",
-       "      -1.632467\n",
-       "      -0.454553\n",
-       "      -2.521362\n",
-       "      -1.285181\n",
-       "      0.121284\n",
-       "      -0.113719\n",
-       "      -0.891262\n",
-       "      -1.863739\n",
-       "      -0.458268\n",
-       "      -0.985505\n",
+       "      0.180511\n",
+       "      -0.656888\n",
+       "      -1.253321\n",
+       "      1.057077\n",
+       "      -0.803677\n",
+       "      -0.759486\n",
+       "      -0.611072\n",
+       "      -1.013733\n",
+       "      -0.167970\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      2496\n",
-       "      -1.382889\n",
-       "      3.700458\n",
-       "      -0.694449\n",
-       "      1\n",
-       "      6.743524\n",
-       "      0.297409\n",
-       "      0.430215\n",
-       "      -1.670237\n",
-       "      -1.288557\n",
-       "      0.760799\n",
+       "      -7.457602\n",
+       "      1.094568\n",
+       "      -2.850723\n",
+       "      0\n",
+       "      9.043466\n",
+       "      1.217386\n",
+       "      -1.788360\n",
+       "      -0.342299\n",
+       "      -0.635556\n",
+       "      0.595195\n",
        "      ...\n",
-       "      0.797380\n",
-       "      0.238452\n",
-       "      -0.227972\n",
-       "      -0.593133\n",
-       "      1.936234\n",
-       "      0.074842\n",
-       "      -0.121784\n",
-       "      0.562210\n",
-       "      -0.223847\n",
-       "      1.014302\n",
+       "      -0.020930\n",
+       "      -0.459529\n",
+       "      1.528013\n",
+       "      0.135980\n",
+       "      -0.448317\n",
+       "      -0.113937\n",
+       "      -0.652803\n",
+       "      -0.103194\n",
+       "      -0.911497\n",
+       "      -0.998202\n",
        "    
\n",
        "    
\n",
        "      2497\n",
-       "      15.394511\n",
-       "      4.097691\n",
-       "      4.765607\n",
-       "      1\n",
-       "      6.960287\n",
-       "      0.384115\n",
-       "      0.306140\n",
-       "      -0.810919\n",
-       "      0.061464\n",
-       "      -0.303729\n",
+       "      -12.389506\n",
+       "      -5.563091\n",
+       "      -2.275472\n",
+       "      0\n",
+       "      2.631578\n",
+       "      -1.347369\n",
+       "      0.948935\n",
+       "      0.733226\n",
+       "      0.776689\n",
+       "      1.007780\n",
        "      ...\n",
-       "      -1.221077\n",
-       "      0.501586\n",
-       "      -0.505733\n",
-       "      -0.299768\n",
-       "      -1.878130\n",
-       "      0.959871\n",
-       "      1.363197\n",
-       "      0.663301\n",
-       "      1.553063\n",
-       "      1.014302\n",
+       "      0.394645\n",
+       "      -1.223821\n",
+       "      -0.281657\n",
+       "      -0.934772\n",
+       "      -0.587933\n",
+       "      1.399632\n",
+       "      -1.077664\n",
+       "      -1.793181\n",
+       "      -0.730043\n",
+       "      -0.998202\n",
        "    
\n",
        "    
\n",
        "      2498\n",
-       "      -1.563496\n",
-       "      1.610554\n",
-       "      -0.058017\n",
+       "      4.181542\n",
+       "      1.763852\n",
+       "      1.805896\n",
        "      1\n",
-       "      7.299606\n",
-       "      0.519842\n",
-       "      0.687352\n",
-       "      0.374898\n",
-       "      -1.522148\n",
-       "      0.237806\n",
+       "      6.074689\n",
+       "      0.029876\n",
+       "      1.265076\n",
+       "      -1.047612\n",
+       "      -0.174857\n",
+       "      -1.611537\n",
        "      ...\n",
-       "      1.651592\n",
-       "      1.484596\n",
-       "      0.627287\n",
-       "      0.012603\n",
-       "      -0.208517\n",
-       "      -0.946828\n",
-       "      -0.137769\n",
-       "      0.030354\n",
-       "      -0.016728\n",
-       "      1.014302\n",
+       "      0.660882\n",
+       "      1.338977\n",
+       "      -1.475265\n",
+       "      -1.475989\n",
+       "      0.910175\n",
+       "      0.325985\n",
+       "      0.349857\n",
+       "      0.066698\n",
+       "      0.557359\n",
+       "      1.001401\n",
        "    
\n",
        "    
\n",
        "      2499\n",
-       "      -12.175734\n",
-       "      -0.847090\n",
-       "      -2.776214\n",
+       "      3.783388\n",
+       "      4.814916\n",
+       "      0.656157\n",
        "      1\n",
-       "      5.305630\n",
-       "      -0.277748\n",
-       "      0.886482\n",
-       "      1.794044\n",
-       "      -0.815157\n",
-       "      -1.947044\n",
+       "      3.824017\n",
+       "      -0.870393\n",
+       "      -0.000393\n",
+       "      -0.373005\n",
+       "      -1.857066\n",
+       "      0.327473\n",
        "      ...\n",
-       "      1.301984\n",
-       "      1.278139\n",
-       "      -0.817968\n",
-       "      -0.772485\n",
-       "      -1.288553\n",
-       "      -0.702799\n",
-       "      -1.077067\n",
-       "      -0.595088\n",
-       "      -0.901333\n",
-       "      1.014302\n",
+       "      0.725508\n",
+       "      -1.219133\n",
+       "      -2.110389\n",
+       "      -0.375057\n",
+       "      0.280377\n",
+       "      -0.899183\n",
+       "      0.315558\n",
+       "      0.841183\n",
+       "      0.194693\n",
+       "      1.001401\n",
        "    
\n",
        "  \n",
        "\n",
@@ -337,61 +350,61 @@
       ],
       "text/plain": [
        "         Y_cont     Y_bin    T_cont  T_bin     alpha  feature_0  feature_1  \\\n",
-       "0     -3.622930 -4.426916  0.267995      0  7.231021   0.492408  -0.153387   \n",
-       "1     -7.762899 -1.634123 -2.042926      0  1.650064  -1.739974  -0.574608   \n",
-       "2     11.977784  5.799723  3.059354      1  4.474363  -0.610255  -0.305802   \n",
-       "3     -5.484061  1.275083 -2.253048      0  5.692706  -0.122917  -0.315150   \n",
-       "4    -12.907767 -2.415022 -3.497581      0  3.875030  -0.849988  -0.323205   \n",
+       "0     17.285021  5.560022  4.908333      1  4.221970  -0.711212   0.678770   \n",
+       "1     -5.418737 -1.169369 -1.416456      0  3.571828  -0.971269   0.155694   \n",
+       "2      8.329916  5.750195  1.859907      1  6.389524   0.155810   0.097990   \n",
+       "3    -14.109005 -3.260798 -3.616069      0  1.522008  -1.791197  -0.025364   \n",
+       "4     15.927794  6.070905  4.285630      1  8.262360   0.904944  -0.648125   \n",
        "...         ...       ...       ...    ...       ...        ...        ...   \n",
-       "2495 -10.076508 -5.832192 -1.414772      0  9.270431   1.308172  -0.802954   \n",
-       "2496  -1.382889  3.700458 -0.694449      1  6.743524   0.297409   0.430215   \n",
-       "2497  15.394511  4.097691  4.765607      1  6.960287   0.384115   0.306140   \n",
-       "2498  -1.563496  1.610554 -0.058017      1  7.299606   0.519842   0.687352   \n",
-       "2499 -12.175734 -0.847090 -2.776214      1  5.305630  -0.277748   0.886482   \n",
+       "2495  -6.973181 -2.492475 -0.493568      1  6.399022   0.159609   2.004918   \n",
+       "2496  -7.457602  1.094568 -2.850723      0  9.043466   1.217386  -1.788360   \n",
+       "2497 -12.389506 -5.563091 -2.275472      0  2.631578  -1.347369   0.948935   \n",
+       "2498   4.181542  1.763852  1.805896      1  6.074689   0.029876   1.265076   \n",
+       "2499   3.783388  4.814916  0.656157      1  3.824017  -0.870393  -0.000393   \n",
        "\n",
        "      feature_2  feature_3  feature_4  ...  feature_8  feature_9  feature_10  \\\n",
-       "0      0.632238   1.549490  -0.217191  ...  -0.483356   0.541115   -0.438765   \n",
-       "1     -1.009005   0.327720  -0.879195  ...   0.055404  -1.411090   -0.518275   \n",
-       "2     -0.602653   1.882276  -0.000420  ...   0.197202  -1.941906   -1.287992   \n",
-       "3     -1.469290  -0.717482  -0.439688  ...   0.312800  -0.379406   -0.648433   \n",
-       "4      0.319486   0.996123  -0.457897  ...   0.802840   1.348549   -0.054392   \n",
+       "0      0.807216   1.502488  -0.194990  ...  -0.355420   1.402186   -1.517787   \n",
+       "1      0.094208   1.060349   0.918542  ...   0.647043   0.978512    0.102692   \n",
+       "2      1.886364   0.204515   1.407368  ...  -0.789757  -1.376844   -1.164524   \n",
+       "3     -1.849614  -0.420513  -0.813558  ...  -0.967529   0.604905    0.136397   \n",
+       "4     -0.420076   0.774960   0.612525  ...   2.362694   2.702110   -1.565191   \n",
        "...         ...        ...        ...  ...        ...        ...         ...   \n",
-       "2495  -2.487989   1.100350  -1.214653  ...  -1.632467  -0.454553   -2.521362   \n",
-       "2496  -1.670237  -1.288557   0.760799  ...   0.797380   0.238452   -0.227972   \n",
-       "2497  -0.810919   0.061464  -0.303729  ...  -1.221077   0.501586   -0.505733   \n",
-       "2498   0.374898  -1.522148   0.237806  ...   1.651592   1.484596    0.627287   \n",
-       "2499   1.794044  -0.815157  -1.947044  ...   1.301984   1.278139   -0.817968   \n",
+       "2495   1.850936  -0.205821   1.826866  ...   0.180511  -0.656888   -1.253321   \n",
+       "2496  -0.342299  -0.635556   0.595195  ...  -0.020930  -0.459529    1.528013   \n",
+       "2497   0.733226   0.776689   1.007780  ...   0.394645  -1.223821   -0.281657   \n",
+       "2498  -1.047612  -0.174857  -1.611537  ...   0.660882   1.338977   -1.475265   \n",
+       "2499  -0.373005  -1.857066   0.327473  ...   0.725508  -1.219133   -2.110389   \n",
        "\n",
        "      feature_11  feature_12  feature_13  Y_cont_scaled  Y_bin_scaled  \\\n",
-       "0      -0.419556    0.237334   -1.976732      -0.320051     -1.506112   \n",
-       "1       0.152670   -1.120139    0.360184      -0.686483     -0.795379   \n",
-       "2       0.237949    0.721191    0.151575       1.060779      1.096449   \n",
-       "3       0.649580    1.006272    0.927402      -0.484782     -0.055020   \n",
-       "4       1.038429    0.353959   -0.682012      -1.141859     -0.994109   \n",
+       "0       0.292296   -0.439312   -0.400863       1.478663      1.030321   \n",
+       "1      -1.338446    1.164631    1.157542      -0.477164     -0.677874   \n",
+       "2      -0.854488   -0.563027   -1.762406       0.707221      1.078595   \n",
+       "3       0.087200   -0.575056   -0.244472      -1.225791     -1.208765   \n",
+       "4       1.689404    1.198610    0.117392       1.361744      1.160004   \n",
        "...          ...         ...         ...            ...           ...   \n",
-       "2495   -1.285181    0.121284   -0.113719      -0.891262     -1.863739   \n",
-       "2496   -0.593133    1.936234    0.074842      -0.121784      0.562210   \n",
-       "2497   -0.299768   -1.878130    0.959871       1.363197      0.663301   \n",
-       "2498    0.012603   -0.208517   -0.946828      -0.137769      0.030354   \n",
-       "2499   -0.772485   -1.288553   -0.702799      -1.077067     -0.595088   \n",
+       "2495    1.057077   -0.803677   -0.759486      -0.611072     -1.013733   \n",
+       "2496    0.135980   -0.448317   -0.113937      -0.652803     -0.103194   \n",
+       "2497   -0.934772   -0.587933    1.399632      -1.077664     -1.793181   \n",
+       "2498   -1.475989    0.910175    0.325985       0.349857      0.066698   \n",
+       "2499   -0.375057    0.280377   -0.899183       0.315558      0.841183   \n",
        "\n",
        "      T_cont_scaled  T_bin_scaled  \n",
-       "0          0.089369     -0.985505  \n",
-       "1         -0.662693     -0.985505  \n",
-       "2          0.997784      1.014302  \n",
-       "3         -0.731075     -0.985505  \n",
-       "4         -1.136093     -0.985505  \n",
+       "0          1.535974      1.001401  \n",
+       "1         -0.459080     -0.998202  \n",
+       "2          0.574396      1.001401  \n",
+       "3         -1.152913     -0.998202  \n",
+       "4          1.339552      1.001401  \n",
        "...             ...           ...  \n",
-       "2495      -0.458268     -0.985505  \n",
-       "2496      -0.223847      1.014302  \n",
-       "2497       1.553063      1.014302  \n",
-       "2498      -0.016728      1.014302  \n",
-       "2499      -0.901333      1.014302  \n",
+       "2495      -0.167970      1.001401  \n",
+       "2496      -0.911497     -0.998202  \n",
+       "2497      -0.730043     -0.998202  \n",
+       "2498       0.557359      1.001401  \n",
+       "2499       0.194693      1.001401  \n",
        "\n",
        "[2500 rows x 23 columns]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -481,9 +494,42 @@
     "data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "806df6ea",
+   "metadata": {},
+   "source": [
+    "### Hyperparameters for Variable Selection Priors"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "id": "ae848fe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n",
+    "axs = axs.flatten()\n",
+    "axs[0].hist(pm.draw(pm.Beta.dist(2, 2), 1000), ec=\"black\", color=\"slateblue\")\n",
+    "axs[1].hist(pm.draw(pm.Beta.dist(2, 5), 1000), ec=\"black\", color=\"slateblue\")\n",
+    "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "763ca253",
    "metadata": {},
    "outputs": [
@@ -501,11 +547,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:172: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
       "                This is not necessarily a problem but it violates\n",
       "                the assumption of a simple IV experiment.\n",
       "                The coefficients should be interpreted appropriately.\n",
-      "  \"\"\"Validate the input data and model formula for correctness\"\"\"\n",
+      "  warnings.warn(\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
       "NUTS: [beta_t, beta_z, chol_cov]\n",
@@ -516,7 +562,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "75ceaa4f49be4429bdb3671caae6d44f",
+       "model_id": "6de2845a3ae7405c85006a8c991e5614",
        "version_major": 2,
        "version_minor": 0
       },
@@ -531,8 +577,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
-      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
      ]
@@ -551,16 +595,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 69 seconds.\n",
-      "There were 17 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 99 seconds.\n",
       "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
-      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
-      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:172: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
       "                This is not necessarily a problem but it violates\n",
       "                the assumption of a simple IV experiment.\n",
       "                The coefficients should be interpreted appropriately.\n",
-      "  \"\"\"Validate the input data and model formula for correctness\"\"\"\n"
+      "  warnings.warn(\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
+      "  warnings.warn(\n"
      ]
     },
     {
@@ -587,7 +631,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8478b37ed9ea450c9d18f31ceacc9c16",
+       "model_id": "b1fa39df9f8f40b4ae84c1d4783720c9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -602,6 +646,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
      ]
@@ -620,11 +666,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 316 seconds.\n",
-      "There were 86 divergences after tuning. Increase `target_accept` or reparameterize.\n",
-      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
-      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
-      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 461 seconds.\n",
+      "There were 79 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
      ]
     }
    ],
@@ -634,7 +678,7 @@
     "    \"tune\": 2000,\n",
     "    \"chains\": 2,\n",
     "    \"cores\": 2,\n",
-    "    \"mptarget_accept\": 0.95,\n",
+    "    \"target_accept\": 0.95,\n",
     "    \"progressbar\": True,\n",
     "    \"random_seed\": 42,\n",
     "    \"mp_ctx\": \"spawn\",\n",
@@ -680,8 +724,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "id": "a9633162",
+   "execution_count": 7,
+   "id": "838e0726",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_spike_slab.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "127888b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    ")\n",
+    "axs[0].set_title(\"Parameter Comparison Outcome Model \\n Baseline v Spike and Slab\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09b24bf",
+   "metadata": {},
+   "source": [
+    "#### The Treatment Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "acafc928",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = az.plot_forest(\n",
+    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "    var_names=[\"beta_t\"],\n",
+    "    combined=True,\n",
+    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    ")\n",
+    "\n",
+    "axs[0].set_title(\"Parameter Comparison Treatment Model \\n Baseline v Spike and Slab\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2a0b213",
    "metadata": {},
    "outputs": [
     {
@@ -705,265 +853,254 @@
        "  \n",
        "    
\n",
        "      \n",
-       "      mean\n",
-       "      sd\n",
-       "      hdi_3%\n",
-       "      hdi_97%\n",
-       "      mcse_mean\n",
-       "      mcse_sd\n",
-       "      ess_bulk\n",
-       "      ess_tail\n",
-       "      r_hat\n",
+       "      prob\n",
+       "      selected\n",
+       "      gamma_mean\n",
        "    
\n",
        "  \n",
        "  \n",
        "    
\n",
-       "      beta_z[Intercept]\n",
-       "      0.015\n",
-       "      0.059\n",
-       "      -0.098\n",
-       "      0.125\n",
-       "      0.001\n",
-       "      0.002\n",
-       "      1658.0\n",
-       "      1023.0\n",
-       "      1.00\n",
+       "      0\n",
+       "      0.0105\n",
+       "      False\n",
+       "      0.014042\n",
        "    
\n",
        "    
\n",
-       "      beta_z[T_cont]\n",
-       "      3.154\n",
-       "      0.357\n",
-       "      2.429\n",
-       "      3.777\n",
-       "      0.029\n",
-       "      0.011\n",
-       "      152.0\n",
-       "      373.0\n",
-       "      1.01\n",
+       "      1\n",
+       "      1.0000\n",
+       "      True\n",
+       "      0.990424\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_0]\n",
-       "      0.430\n",
-       "      0.233\n",
-       "      0.017\n",
-       "      0.905\n",
-       "      0.019\n",
-       "      0.007\n",
-       "      159.0\n",
-       "      436.0\n",
-       "      1.01\n",
+       "      2\n",
+       "      0.7605\n",
+       "      True\n",
+       "      0.775804\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_1]\n",
-       "      -0.383\n",
-       "      0.059\n",
-       "      -0.491\n",
-       "      -0.277\n",
-       "      0.001\n",
-       "      0.002\n",
-       "      1720.0\n",
-       "      1250.0\n",
-       "      1.00\n",
+       "      3\n",
+       "      0.6880\n",
+       "      True\n",
+       "      0.719690\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_2]\n",
-       "      0.030\n",
-       "      0.162\n",
-       "      -0.272\n",
-       "      0.341\n",
-       "      0.012\n",
-       "      0.005\n",
-       "      172.0\n",
-       "      418.0\n",
-       "      1.01\n",
+       "      4\n",
+       "      0.0570\n",
+       "      False\n",
+       "      0.064343\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_3]\n",
-       "      -0.055\n",
-       "      0.143\n",
-       "      -0.301\n",
-       "      0.242\n",
-       "      0.011\n",
-       "      0.005\n",
-       "      171.0\n",
-       "      428.0\n",
-       "      1.00\n",
+       "      5\n",
+       "      0.0330\n",
+       "      False\n",
+       "      0.038359\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_4]\n",
-       "      0.442\n",
-       "      0.064\n",
-       "      0.335\n",
-       "      0.564\n",
-       "      0.003\n",
-       "      0.004\n",
-       "      685.0\n",
-       "      668.0\n",
-       "      1.00\n",
+       "      6\n",
+       "      0.6755\n",
+       "      True\n",
+       "      0.704165\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_5]\n",
-       "      -0.000\n",
-       "      0.058\n",
-       "      -0.101\n",
-       "      0.113\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      1338.0\n",
-       "      970.0\n",
-       "      1.00\n",
+       "      7\n",
+       "      0.0085\n",
+       "      False\n",
+       "      0.011460\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_6]\n",
-       "      0.027\n",
-       "      0.061\n",
-       "      -0.094\n",
-       "      0.140\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      1184.0\n",
-       "      1063.0\n",
-       "      1.00\n",
+       "      8\n",
+       "      0.0170\n",
+       "      False\n",
+       "      0.021624\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_7]\n",
-       "      -0.037\n",
-       "      0.058\n",
-       "      -0.140\n",
-       "      0.077\n",
-       "      0.002\n",
-       "      0.001\n",
-       "      914.0\n",
-       "      675.0\n",
-       "      1.00\n",
+       "      9\n",
+       "      0.0130\n",
+       "      False\n",
+       "      0.015590\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_8]\n",
-       "      -0.076\n",
-       "      0.060\n",
-       "      -0.193\n",
-       "      0.034\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      827.0\n",
-       "      602.0\n",
-       "      1.00\n",
+       "      10\n",
+       "      0.0080\n",
+       "      False\n",
+       "      0.010675\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_9]\n",
-       "      0.042\n",
-       "      0.060\n",
-       "      -0.072\n",
-       "      0.146\n",
-       "      0.002\n",
-       "      0.004\n",
-       "      1191.0\n",
-       "      809.0\n",
-       "      1.00\n",
+       "      11\n",
+       "      0.0250\n",
+       "      False\n",
+       "      0.029619\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_10]\n",
-       "      -0.034\n",
-       "      0.061\n",
-       "      -0.151\n",
-       "      0.077\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      776.0\n",
-       "      907.0\n",
-       "      1.00\n",
+       "      12\n",
+       "      0.0060\n",
+       "      False\n",
+       "      0.008689\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_11]\n",
-       "      0.075\n",
-       "      0.059\n",
-       "      -0.029\n",
-       "      0.190\n",
-       "      0.002\n",
-       "      0.001\n",
-       "      864.0\n",
-       "      904.0\n",
-       "      1.00\n",
+       "      13\n",
+       "      0.0250\n",
+       "      False\n",
+       "      0.028057\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_12]\n",
-       "      -0.045\n",
-       "      0.061\n",
-       "      -0.160\n",
-       "      0.064\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      951.0\n",
-       "      946.0\n",
-       "      1.00\n",
+       "      14\n",
+       "      0.0135\n",
+       "      False\n",
+       "      0.016080\n",
        "    
\n",
        "    
\n",
-       "      beta_z[feature_13]\n",
-       "      -0.040\n",
-       "      0.059\n",
-       "      -0.164\n",
-       "      0.060\n",
-       "      0.002\n",
-       "      0.002\n",
-       "      1466.0\n",
-       "      1266.0\n",
-       "      1.00\n",
+       "      15\n",
+       "      0.0135\n",
+       "      False\n",
+       "      0.015825\n",
        "    
\n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
-       "beta_z[Intercept]   0.015  0.059  -0.098    0.125      0.001    0.002   \n",
-       "beta_z[T_cont]      3.154  0.357   2.429    3.777      0.029    0.011   \n",
-       "beta_z[feature_0]   0.430  0.233   0.017    0.905      0.019    0.007   \n",
-       "beta_z[feature_1]  -0.383  0.059  -0.491   -0.277      0.001    0.002   \n",
-       "beta_z[feature_2]   0.030  0.162  -0.272    0.341      0.012    0.005   \n",
-       "beta_z[feature_3]  -0.055  0.143  -0.301    0.242      0.011    0.005   \n",
-       "beta_z[feature_4]   0.442  0.064   0.335    0.564      0.003    0.004   \n",
-       "beta_z[feature_5]  -0.000  0.058  -0.101    0.113      0.002    0.002   \n",
-       "beta_z[feature_6]   0.027  0.061  -0.094    0.140      0.002    0.002   \n",
-       "beta_z[feature_7]  -0.037  0.058  -0.140    0.077      0.002    0.001   \n",
-       "beta_z[feature_8]  -0.076  0.060  -0.193    0.034      0.002    0.002   \n",
-       "beta_z[feature_9]   0.042  0.060  -0.072    0.146      0.002    0.004   \n",
-       "beta_z[feature_10] -0.034  0.061  -0.151    0.077      0.002    0.002   \n",
-       "beta_z[feature_11]  0.075  0.059  -0.029    0.190      0.002    0.001   \n",
-       "beta_z[feature_12] -0.045  0.061  -0.160    0.064      0.002    0.002   \n",
-       "beta_z[feature_13] -0.040  0.059  -0.164    0.060      0.002    0.002   \n",
-       "\n",
-       "                    ess_bulk  ess_tail  r_hat  \n",
-       "beta_z[Intercept]     1658.0    1023.0   1.00  \n",
-       "beta_z[T_cont]         152.0     373.0   1.01  \n",
-       "beta_z[feature_0]      159.0     436.0   1.01  \n",
-       "beta_z[feature_1]     1720.0    1250.0   1.00  \n",
-       "beta_z[feature_2]      172.0     418.0   1.01  \n",
-       "beta_z[feature_3]      171.0     428.0   1.00  \n",
-       "beta_z[feature_4]      685.0     668.0   1.00  \n",
-       "beta_z[feature_5]     1338.0     970.0   1.00  \n",
-       "beta_z[feature_6]     1184.0    1063.0   1.00  \n",
-       "beta_z[feature_7]      914.0     675.0   1.00  \n",
-       "beta_z[feature_8]      827.0     602.0   1.00  \n",
-       "beta_z[feature_9]     1191.0     809.0   1.00  \n",
-       "beta_z[feature_10]     776.0     907.0   1.00  \n",
-       "beta_z[feature_11]     864.0     904.0   1.00  \n",
-       "beta_z[feature_12]     951.0     946.0   1.00  \n",
-       "beta_z[feature_13]    1466.0    1266.0   1.00  "
+       "      prob  selected  gamma_mean\n",
+       "0   0.0105     False    0.014042\n",
+       "1   1.0000      True    0.990424\n",
+       "2   0.7605      True    0.775804\n",
+       "3   0.6880      True    0.719690\n",
+       "4   0.0570     False    0.064343\n",
+       "5   0.0330     False    0.038359\n",
+       "6   0.6755      True    0.704165\n",
+       "7   0.0085     False    0.011460\n",
+       "8   0.0170     False    0.021624\n",
+       "9   0.0130     False    0.015590\n",
+       "10  0.0080     False    0.010675\n",
+       "11  0.0250     False    0.029619\n",
+       "12  0.0060     False    0.008689\n",
+       "13  0.0250     False    0.028057\n",
+       "14  0.0135     False    0.016080\n",
+       "15  0.0135     False    0.015825"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "az.summary(result_normal.idata, var_names=[\"beta_z\"])"
+    "summary = result_spike_slab.model.vs_prior_outcome.get_inclusion_probabilities(\n",
+    "    result_spike_slab.idata, \"beta_z\"\n",
+    ")\n",
+    "summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38568d27",
+   "metadata": {},
+   "source": [
+    "## Horseshoe"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "bfcdeecd",
+   "execution_count": 72,
+   "id": "63edfa4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 3: Horseshoe Priors\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
+      "                This is not necessarily a problem but it violates\n",
+      "                the assumption of a simple IV experiment.\n",
+      "                The coefficients should be interpreted appropriately.\n",
+      "  warnings.warn(\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
+      "  warnings.warn(\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "NUTS: [tau_beta_t, lambda_beta_t, c2_beta_t, beta_t_raw, tau_beta_z, lambda_beta_z, c2_beta_z, beta_z_raw, chol_cov]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1407969a4c4946d0920f0defc2ec153c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 743 seconds.\n",
+      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
+      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "# =========================================================================\n",
+    "# Model 2: Horseshoe priors\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 3: Horseshoe Priors\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_horseshoe = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=\"horseshoe\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "9c283ee1",
    "metadata": {},
    "outputs": [
     {
@@ -987,312 +1124,165 @@
        "  \n",
        "    \n",
        "      \n",
-       "      mean\n",
-       "      sd\n",
-       "      hdi_3%\n",
-       "      hdi_97%\n",
-       "      mcse_mean\n",
-       "      mcse_sd\n",
-       "      ess_bulk\n",
-       "      ess_tail\n",
-       "      r_hat\n",
+       "      shrinkage_factor\n",
+       "      lambda_tilde\n",
+       "      tau\n",
        "    \n",
        "  \n",
        "  \n",
        "    \n",
-       "      beta_z[Intercept]\n",
-       "      0.001\n",
-       "      0.010\n",
-       "      -0.015\n",
-       "      0.011\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      1035.0\n",
-       "      807.0\n",
-       "      1.00\n",
+       "      0\n",
+       "      0.024168\n",
+       "      1.163944\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[T_cont]\n",
-       "      3.076\n",
-       "      0.099\n",
-       "      2.900\n",
-       "      3.269\n",
-       "      0.006\n",
-       "      0.004\n",
-       "      286.0\n",
-       "      340.0\n",
-       "      1.00\n",
+       "      1\n",
+       "      1.574053\n",
+       "      75.808614\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_0]\n",
-       "      0.476\n",
-       "      0.086\n",
-       "      0.323\n",
-       "      0.648\n",
-       "      0.004\n",
-       "      0.002\n",
-       "      477.0\n",
-       "      571.0\n",
-       "      1.00\n",
+       "      2\n",
+       "      0.454083\n",
+       "      21.869287\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_1]\n",
-       "      -0.392\n",
-       "      0.047\n",
-       "      -0.480\n",
-       "      -0.306\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      1903.0\n",
-       "      1459.0\n",
-       "      1.00\n",
+       "      3\n",
+       "      0.416588\n",
+       "      20.063451\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_2]\n",
-       "      -0.000\n",
-       "      0.018\n",
-       "      -0.023\n",
-       "      0.019\n",
-       "      0.001\n",
-       "      0.002\n",
-       "      464.0\n",
-       "      886.0\n",
-       "      1.01\n",
+       "      4\n",
+       "      0.078152\n",
+       "      3.763922\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_3]\n",
-       "      -0.003\n",
-       "      0.021\n",
-       "      -0.031\n",
-       "      0.018\n",
-       "      0.001\n",
-       "      0.003\n",
-       "      357.0\n",
-       "      313.0\n",
-       "      1.00\n",
+       "      5\n",
+       "      0.043892\n",
+       "      2.113912\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_4]\n",
-       "      0.423\n",
-       "      0.049\n",
-       "      0.341\n",
-       "      0.520\n",
-       "      0.003\n",
-       "      0.001\n",
-       "      380.0\n",
-       "      176.0\n",
-       "      1.01\n",
+       "      6\n",
+       "      0.388261\n",
+       "      18.699190\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_5]\n",
-       "      0.000\n",
-       "      0.009\n",
-       "      -0.012\n",
-       "      0.008\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      629.0\n",
-       "      1103.0\n",
-       "      1.00\n",
+       "      7\n",
+       "      0.027243\n",
+       "      1.312060\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_6]\n",
-       "      0.003\n",
-       "      0.016\n",
-       "      -0.012\n",
-       "      0.033\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      781.0\n",
-       "      1101.0\n",
-       "      1.00\n",
+       "      8\n",
+       "      0.027663\n",
+       "      1.332293\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_7]\n",
-       "      -0.005\n",
-       "      0.019\n",
-       "      -0.038\n",
-       "      0.015\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      738.0\n",
-       "      472.0\n",
-       "      1.01\n",
+       "      9\n",
+       "      0.026640\n",
+       "      1.283037\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_8]\n",
-       "      -0.004\n",
-       "      0.018\n",
-       "      -0.039\n",
-       "      0.012\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      700.0\n",
-       "      973.0\n",
-       "      1.00\n",
+       "      10\n",
+       "      0.026496\n",
+       "      1.276087\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_9]\n",
-       "      0.002\n",
-       "      0.014\n",
-       "      -0.012\n",
-       "      0.013\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      925.0\n",
-       "      1187.0\n",
-       "      1.00\n",
+       "      11\n",
+       "      0.031909\n",
+       "      1.536788\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_10]\n",
-       "      -0.001\n",
-       "      0.010\n",
-       "      -0.011\n",
-       "      0.012\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      1407.0\n",
-       "      1059.0\n",
-       "      1.00\n",
+       "      12\n",
+       "      0.024832\n",
+       "      1.195942\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_11]\n",
-       "      0.004\n",
-       "      0.019\n",
-       "      -0.016\n",
-       "      0.031\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      787.0\n",
-       "      727.0\n",
-       "      1.01\n",
+       "      13\n",
+       "      0.032776\n",
+       "      1.578531\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_12]\n",
-       "      -0.006\n",
-       "      0.023\n",
-       "      -0.050\n",
-       "      0.010\n",
-       "      0.001\n",
-       "      0.001\n",
-       "      394.0\n",
-       "      684.0\n",
-       "      1.01\n",
+       "      14\n",
+       "      0.026933\n",
+       "      1.297110\n",
+       "      0.031037\n",
        "    \n",
        "    \n",
-       "      beta_z[feature_13]\n",
-       "      -0.001\n",
-       "      0.011\n",
-       "      -0.012\n",
-       "      0.005\n",
-       "      0.000\n",
-       "      0.001\n",
-       "      746.0\n",
-       "      1345.0\n",
-       "      1.00\n",
+       "      15\n",
+       "      0.024878\n",
+       "      1.198152\n",
+       "      0.031037\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "                     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
-       "beta_z[Intercept]   0.001  0.010  -0.015    0.011      0.000    0.001   \n",
-       "beta_z[T_cont]      3.076  0.099   2.900    3.269      0.006    0.004   \n",
-       "beta_z[feature_0]   0.476  0.086   0.323    0.648      0.004    0.002   \n",
-       "beta_z[feature_1]  -0.392  0.047  -0.480   -0.306      0.001    0.001   \n",
-       "beta_z[feature_2]  -0.000  0.018  -0.023    0.019      0.001    0.002   \n",
-       "beta_z[feature_3]  -0.003  0.021  -0.031    0.018      0.001    0.003   \n",
-       "beta_z[feature_4]   0.423  0.049   0.341    0.520      0.003    0.001   \n",
-       "beta_z[feature_5]   0.000  0.009  -0.012    0.008      0.000    0.001   \n",
-       "beta_z[feature_6]   0.003  0.016  -0.012    0.033      0.000    0.001   \n",
-       "beta_z[feature_7]  -0.005  0.019  -0.038    0.015      0.001    0.001   \n",
-       "beta_z[feature_8]  -0.004  0.018  -0.039    0.012      0.001    0.001   \n",
-       "beta_z[feature_9]   0.002  0.014  -0.012    0.013      0.000    0.001   \n",
-       "beta_z[feature_10] -0.001  0.010  -0.011    0.012      0.000    0.001   \n",
-       "beta_z[feature_11]  0.004  0.019  -0.016    0.031      0.001    0.001   \n",
-       "beta_z[feature_12] -0.006  0.023  -0.050    0.010      0.001    0.001   \n",
-       "beta_z[feature_13] -0.001  0.011  -0.012    0.005      0.000    0.001   \n",
-       "\n",
-       "                    ess_bulk  ess_tail  r_hat  \n",
-       "beta_z[Intercept]     1035.0     807.0   1.00  \n",
-       "beta_z[T_cont]         286.0     340.0   1.00  \n",
-       "beta_z[feature_0]      477.0     571.0   1.00  \n",
-       "beta_z[feature_1]     1903.0    1459.0   1.00  \n",
-       "beta_z[feature_2]      464.0     886.0   1.01  \n",
-       "beta_z[feature_3]      357.0     313.0   1.00  \n",
-       "beta_z[feature_4]      380.0     176.0   1.01  \n",
-       "beta_z[feature_5]      629.0    1103.0   1.00  \n",
-       "beta_z[feature_6]      781.0    1101.0   1.00  \n",
-       "beta_z[feature_7]      738.0     472.0   1.01  \n",
-       "beta_z[feature_8]      700.0     973.0   1.00  \n",
-       "beta_z[feature_9]      925.0    1187.0   1.00  \n",
-       "beta_z[feature_10]    1407.0    1059.0   1.00  \n",
-       "beta_z[feature_11]     787.0     727.0   1.01  \n",
-       "beta_z[feature_12]     394.0     684.0   1.01  \n",
-       "beta_z[feature_13]     746.0    1345.0   1.00  "
+       "    shrinkage_factor  lambda_tilde       tau\n",
+       "0           0.024168      1.163944  0.031037\n",
+       "1           1.574053     75.808614  0.031037\n",
+       "2           0.454083     21.869287  0.031037\n",
+       "3           0.416588     20.063451  0.031037\n",
+       "4           0.078152      3.763922  0.031037\n",
+       "5           0.043892      2.113912  0.031037\n",
+       "6           0.388261     18.699190  0.031037\n",
+       "7           0.027243      1.312060  0.031037\n",
+       "8           0.027663      1.332293  0.031037\n",
+       "9           0.026640      1.283037  0.031037\n",
+       "10          0.026496      1.276087  0.031037\n",
+       "11          0.031909      1.536788  0.031037\n",
+       "12          0.024832      1.195942  0.031037\n",
+       "13          0.032776      1.578531  0.031037\n",
+       "14          0.026933      1.297110  0.031037\n",
+       "15          0.024878      1.198152  0.031037"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "az.summary(result_spike_slab.idata, var_names=[\"beta_z\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "838e0726",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(20, 6))\n",
-    "az.plot_posterior(\n",
-    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    "summary = result_horseshoe.model.vs_prior_outcome.get_shrinkage_factors(\n",
+    "    result_horseshoe.idata, \"beta_z\"\n",
     ")\n",
-    "az.plot_posterior(\n",
-    "    result_spike_slab.idata,\n",
-    "    var_names=[\"beta_z\"],\n",
-    "    coords={\"covariates\": [\"T_cont\"]},\n",
-    "    ax=ax,\n",
-    ");"
+    "summary"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "id": "127888b7",
+   "execution_count": 134,
+   "id": "82b0121c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([], dtype=object)"
+       ""
       ]
      },
-     "execution_count": 41,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {},
@@ -1300,56 +1290,17 @@
     }
    ],
    "source": [
-    "az.plot_forest(\n",
-    "    [result_spike_slab.idata, result_normal.idata],\n",
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_horseshoe.idata,\n",
     "    var_names=[\"beta_z\"],\n",
-    "    combined=True,\n",
-    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f09b24bf",
-   "metadata": {},
-   "source": [
-    "## The Treatment Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "acafc928",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([], dtype=object)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "
"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "az.plot_forest(\n",
-    "    [result_spike_slab.idata, result_normal.idata],\n",
-    "    var_names=[\"beta_t\"],\n",
-    "    combined=True,\n",
-    "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
-    ")"
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\")"
    ]
   }
  ],

From 4577106e79a0652c0d41b8d7ffed86e15e9775f1 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 09:34:47 +0000
Subject: [PATCH 09/17] add normal test vs_prior

Signed-off-by: Nathaniel 
---
 causalpy/tests/test_variable_selection_priors.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/causalpy/tests/test_variable_selection_priors.py b/causalpy/tests/test_variable_selection_priors.py
index 09de5885..6aad7edb 100644
--- a/causalpy/tests/test_variable_selection_priors.py
+++ b/causalpy/tests/test_variable_selection_priors.py
@@ -100,6 +100,17 @@ def test_create_prior_horseshoe(coords, sample_data):
         assert beta.name == "beta"
 
 
+def test_create_prior_normal(coords, sample_data):
+    """Test create_prior for horseshoe."""
+    vs_prior = VariableSelectionPrior("normal")
+
+    with pm.Model(coords=coords) as model:
+        beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")
+
+        assert "beta" in model.named_vars
+        assert beta.name == "beta"
+
+
 def test_convenience_function_with_custom_hyperparams(coords):
     """Test convenience function with custom hyperparameters."""
     with pm.Model(coords=coords) as model:

From fd1bfb7c3853ca11789c73244f69f7fa0876eb25 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 20:31:25 +0000
Subject: [PATCH 10/17] improving test coverage

Signed-off-by: Nathaniel 
---
 causalpy/tests/test_integration_pymc_examples.py | 8 ++++++++
 causalpy/tests/test_variable_selection_priors.py | 2 +-
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py
index efeb6bf9..233c9cd4 100644
--- a/causalpy/tests/test_integration_pymc_examples.py
+++ b/causalpy/tests/test_integration_pymc_examples.py
@@ -710,6 +710,10 @@ def test_iv_reg_vs_prior(mock_pymc_sample):
         result.idata, "beta_z"
     )
     assert isinstance(summary, pd.DataFrame)
+    with pytest.raises(ValueError):
+        summary = result.model.vs_prior_outcome.get_shrinkage_factors(
+            result.idata, "beta_z"
+        )
 
 
 @pytest.mark.integration
@@ -745,6 +749,10 @@ def test_iv_reg_vs_prior_hs(mock_pymc_sample):
         result.idata, "beta_z"
     )
     assert isinstance(summary, pd.DataFrame)
+    with pytest.raises(ValueError):
+        summary = result.model.vs_prior_outcome.get_inclusion_probabilities(
+            result.idata, "beta_z"
+        )
 
 
 @pytest.mark.integration
diff --git a/causalpy/tests/test_variable_selection_priors.py b/causalpy/tests/test_variable_selection_priors.py
index 6aad7edb..1b464be6 100644
--- a/causalpy/tests/test_variable_selection_priors.py
+++ b/causalpy/tests/test_variable_selection_priors.py
@@ -78,7 +78,7 @@ def test_create_variable_in_model_context_horseshoe(coords):
 
 def test_create_prior_spike_and_slab(coords):
     """Test create_prior for spike-and-slab."""
-    vs_prior = VariableSelectionPrior("spike_and_slab")
+    vs_prior = VariableSelectionPrior("spike_and_slab", hyperparams={"pi_alpha": 5})
 
     with pm.Model(coords=coords) as model:
         beta = vs_prior.create_prior(name="beta", n_params=5, dims="features")

From 7375aa584e1050f1df7b7c01222833744b0a26a7 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 21:20:28 +0000
Subject: [PATCH 11/17] updating notebook

Signed-off-by: Nathaniel 
---
 causalpy/pymc_models.py                  |    8 +-
 docs/source/notebooks/iv_vs_priors.ipynb | 1461 ++++++++++++++++------
 2 files changed, 1070 insertions(+), 399 deletions(-)

diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index 7d30dd5a..e04f446f 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -923,6 +923,7 @@ def fit_outcome_model(
         normal_outcome: bool = True,
         spline_component: bool = False,
         winsorize_boundary: float = 0.0,
+        spline_knots: int = 30,
     ) -> tuple[az.InferenceData, pm.Model]:
         """
         Fit a Bayesian outcome model using covariates and previously estimated propensity scores.
@@ -963,6 +964,9 @@ def fit_outcome_model(
             If we wish to winsorize the propensity score this can be set to clip the high
             and low values of the propensity at 0 + winsorize_boundary and 1-winsorize_boundary
 
+        spline_knots: int, default 30
+            The number of knots we use in the 0 - 1 interval to create our spline function
+
         Returns
         -------
         idata_outcome : arviz.InferenceData
@@ -1026,11 +1030,11 @@ class initialisation.
                     "beta_ps_spline",
                     priors["beta_ps"][0],
                     priors["beta_ps"][1],
-                    size=34,
+                    size=spline_knots + 4,
                 )
                 B = dmatrix(
                     "bs(ps, knots=knots, degree=3, include_intercept=True, lower_bound=0, upper_bound=1) - 1",
-                    {"ps": p, "knots": np.linspace(0, 1, 30)},
+                    {"ps": p, "knots": np.linspace(0, 1, spline_knots)},
                 )
                 B_f = np.asarray(B, order="F")
                 splines_summed = pm.Deterministic(
diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 97e221ad..bd12eed6 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -2,16 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 1,
    "id": "532c6736",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     }
    ],
@@ -28,6 +27,18 @@
     "%autoreload 2"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b1b3aa75",
+   "metadata": {},
+   "source": [
+    "## Variable Selection Priors and Instrumental Variable Designs\n",
+    "\n",
+    "When building causal inference models, we often face a dilemma: we want to control for confounders to get unbiased causal estimates, but we're not always certain which variables are the true confounders. Include too few, and we risk omitted variable bias. Include too many, and we introduce noise that inflates our uncertainty or, worse, creates multicollinearity that destabilizes our estimates.\n",
+    "\n",
+    "Traditional approaches force us to make hard choices upfront—which variables to include, which to exclude. This in ideal cases should be driven by theory. But what if we could let the data help us make these decisions while still maintaining the principled probabilistic framework of Bayesian inference? This is where variable selection priors come in. Let's first simulate some data with some natural confounding structure. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,
@@ -81,123 +92,123 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      17.285021\n",
-       "      5.560022\n",
-       "      4.908333\n",
-       "      1\n",
-       "      4.221970\n",
-       "      -0.711212\n",
-       "      0.678770\n",
-       "      0.807216\n",
-       "      1.502488\n",
-       "      -0.194990\n",
+       "      3.169837\n",
+       "      -0.170346\n",
+       "      1.113394\n",
+       "      0\n",
+       "      9.236102\n",
+       "      1.294441\n",
+       "      0.418241\n",
+       "      0.536286\n",
+       "      -0.615573\n",
+       "      -1.173784\n",
        "      ...\n",
-       "      -0.355420\n",
-       "      1.402186\n",
-       "      -1.517787\n",
-       "      0.292296\n",
-       "      -0.439312\n",
-       "      -0.400863\n",
-       "      1.478663\n",
-       "      1.030321\n",
-       "      1.535974\n",
-       "      1.001401\n",
+       "      0.559393\n",
+       "      1.111766\n",
+       "      -0.216069\n",
+       "      0.451496\n",
+       "      -0.863189\n",
+       "      0.319180\n",
+       "      0.268475\n",
+       "      -0.438362\n",
+       "      0.347809\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      -5.418737\n",
-       "      -1.169369\n",
-       "      -1.416456\n",
-       "      0\n",
-       "      3.571828\n",
-       "      -0.971269\n",
-       "      0.155694\n",
-       "      0.094208\n",
-       "      1.060349\n",
-       "      0.918542\n",
+       "      10.479049\n",
+       "      6.662990\n",
+       "      2.272020\n",
+       "      1\n",
+       "      3.787487\n",
+       "      -0.885005\n",
+       "      0.315013\n",
+       "      0.810138\n",
+       "      1.137214\n",
+       "      0.203685\n",
        "      ...\n",
-       "      0.647043\n",
-       "      0.978512\n",
-       "      0.102692\n",
-       "      -1.338446\n",
-       "      1.164631\n",
-       "      1.157542\n",
-       "      -0.477164\n",
-       "      -0.677874\n",
-       "      -0.459080\n",
-       "      -0.998202\n",
+       "      -0.017179\n",
+       "      0.410818\n",
+       "      0.670074\n",
+       "      1.954944\n",
+       "      0.888255\n",
+       "      -0.506518\n",
+       "      0.916637\n",
+       "      1.321011\n",
+       "      0.726014\n",
+       "      0.977650\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      8.329916\n",
-       "      5.750195\n",
-       "      1.859907\n",
+       "      7.307821\n",
+       "      4.118982\n",
+       "      2.062946\n",
        "      1\n",
-       "      6.389524\n",
-       "      0.155810\n",
-       "      0.097990\n",
-       "      1.886364\n",
-       "      0.204515\n",
-       "      1.407368\n",
+       "      3.311157\n",
+       "      -1.075537\n",
+       "      1.058536\n",
+       "      1.908944\n",
+       "      1.122914\n",
+       "      0.611691\n",
        "      ...\n",
-       "      -0.789757\n",
-       "      -1.376844\n",
-       "      -1.164524\n",
-       "      -0.854488\n",
-       "      -0.563027\n",
-       "      -1.762406\n",
-       "      0.707221\n",
-       "      1.078595\n",
-       "      0.574396\n",
-       "      1.001401\n",
+       "      0.050641\n",
+       "      1.245489\n",
+       "      -1.070642\n",
+       "      -0.060250\n",
+       "      -1.857638\n",
+       "      0.806913\n",
+       "      0.635421\n",
+       "      0.666008\n",
+       "      0.657767\n",
+       "      0.977650\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      -14.109005\n",
-       "      -3.260798\n",
-       "      -3.616069\n",
-       "      0\n",
-       "      1.522008\n",
-       "      -1.791197\n",
-       "      -0.025364\n",
-       "      -1.849614\n",
-       "      -0.420513\n",
-       "      -0.813558\n",
+       "      9.781360\n",
+       "      0.649647\n",
+       "      4.043904\n",
+       "      1\n",
+       "      8.423450\n",
+       "      0.969380\n",
+       "      0.698199\n",
+       "      0.314419\n",
+       "      1.446987\n",
+       "      -2.729092\n",
        "      ...\n",
-       "      -0.967529\n",
-       "      0.604905\n",
-       "      0.136397\n",
-       "      0.087200\n",
-       "      -0.575056\n",
-       "      -0.244472\n",
-       "      -1.225791\n",
-       "      -1.208765\n",
-       "      -1.152913\n",
-       "      -0.998202\n",
+       "      -1.052429\n",
+       "      1.143079\n",
+       "      -1.757701\n",
+       "      -1.167276\n",
+       "      -0.164620\n",
+       "      1.687004\n",
+       "      0.854768\n",
+       "      -0.227239\n",
+       "      1.304402\n",
+       "      0.977650\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      15.927794\n",
-       "      6.070905\n",
-       "      4.285630\n",
+       "      5.739283\n",
+       "      6.812030\n",
+       "      0.642417\n",
        "      1\n",
-       "      8.262360\n",
-       "      0.904944\n",
-       "      -0.648125\n",
-       "      -0.420076\n",
-       "      0.774960\n",
-       "      0.612525\n",
+       "      6.613922\n",
+       "      0.245569\n",
+       "      -0.338491\n",
+       "      0.814305\n",
+       "      -0.859798\n",
+       "      0.334968\n",
        "      ...\n",
-       "      2.362694\n",
-       "      2.702110\n",
-       "      -1.565191\n",
-       "      1.689404\n",
-       "      1.198610\n",
-       "      0.117392\n",
-       "      1.361744\n",
-       "      1.160004\n",
-       "      1.339552\n",
-       "      1.001401\n",
+       "      -0.547622\n",
+       "      0.778724\n",
+       "      0.678956\n",
+       "      1.715229\n",
+       "      -0.439130\n",
+       "      -0.238847\n",
+       "      0.496327\n",
+       "      1.359385\n",
+       "      0.194070\n",
+       "      0.977650\n",
        "    \n",
        "    \n",
        "      ...\n",
@@ -225,123 +236,123 @@
        "    \n",
        "    \n",
        "      2495\n",
-       "      -6.973181\n",
-       "      -2.492475\n",
-       "      -0.493568\n",
-       "      1\n",
-       "      6.399022\n",
-       "      0.159609\n",
-       "      2.004918\n",
-       "      1.850936\n",
-       "      -0.205821\n",
-       "      1.826866\n",
+       "      -5.099912\n",
+       "      -0.870746\n",
+       "      -1.409722\n",
+       "      0\n",
+       "      6.565630\n",
+       "      0.226252\n",
+       "      1.589653\n",
+       "      0.056005\n",
+       "      -0.386026\n",
+       "      -0.462251\n",
        "      ...\n",
-       "      0.180511\n",
-       "      -0.656888\n",
-       "      -1.253321\n",
-       "      1.057077\n",
-       "      -0.803677\n",
-       "      -0.759486\n",
-       "      -0.611072\n",
-       "      -1.013733\n",
-       "      -0.167970\n",
-       "      1.001401\n",
+       "      0.987633\n",
+       "      0.246870\n",
+       "      -0.202917\n",
+       "      0.178579\n",
+       "      0.763186\n",
+       "      0.527462\n",
+       "      -0.464865\n",
+       "      -0.618693\n",
+       "      -0.475800\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      2496\n",
-       "      -7.457602\n",
-       "      1.094568\n",
-       "      -2.850723\n",
+       "      -32.742858\n",
+       "      -7.337551\n",
+       "      -8.468435\n",
        "      0\n",
-       "      9.043466\n",
-       "      1.217386\n",
-       "      -1.788360\n",
-       "      -0.342299\n",
-       "      -0.635556\n",
-       "      0.595195\n",
+       "      4.760520\n",
+       "      -0.495792\n",
+       "      0.546002\n",
+       "      0.209072\n",
+       "      0.666614\n",
+       "      0.400847\n",
        "      ...\n",
-       "      -0.020930\n",
-       "      -0.459529\n",
-       "      1.528013\n",
-       "      0.135980\n",
-       "      -0.448317\n",
-       "      -0.113937\n",
-       "      -0.652803\n",
-       "      -0.103194\n",
-       "      -0.911497\n",
-       "      -0.998202\n",
+       "      -0.798775\n",
+       "      -0.616483\n",
+       "      0.431552\n",
+       "      1.238957\n",
+       "      0.957759\n",
+       "      -0.583051\n",
+       "      -2.916171\n",
+       "      -2.283696\n",
+       "      -2.779944\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      2497\n",
-       "      -12.389506\n",
-       "      -5.563091\n",
-       "      -2.275472\n",
+       "      6.759804\n",
+       "      1.912040\n",
+       "      1.615921\n",
        "      0\n",
-       "      2.631578\n",
-       "      -1.347369\n",
-       "      0.948935\n",
-       "      0.733226\n",
-       "      0.776689\n",
-       "      1.007780\n",
+       "      10.445934\n",
+       "      1.778373\n",
+       "      0.097808\n",
+       "      -0.807658\n",
+       "      0.380358\n",
+       "      -0.455391\n",
        "      ...\n",
-       "      0.394645\n",
-       "      -1.223821\n",
-       "      -0.281657\n",
-       "      -0.934772\n",
-       "      -0.587933\n",
-       "      1.399632\n",
-       "      -1.077664\n",
-       "      -1.793181\n",
-       "      -0.730043\n",
-       "      -0.998202\n",
+       "      0.668040\n",
+       "      1.471963\n",
+       "      0.573966\n",
+       "      -0.288768\n",
+       "      -0.861025\n",
+       "      0.372657\n",
+       "      0.586824\n",
+       "      0.097788\n",
+       "      0.511847\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      2498\n",
-       "      4.181542\n",
-       "      1.763852\n",
-       "      1.805896\n",
-       "      1\n",
-       "      6.074689\n",
-       "      0.029876\n",
-       "      1.265076\n",
-       "      -1.047612\n",
-       "      -0.174857\n",
-       "      -1.611537\n",
+       "      -11.249395\n",
+       "      -1.938808\n",
+       "      -3.103529\n",
+       "      0\n",
+       "      5.715321\n",
+       "      -0.113872\n",
+       "      0.747480\n",
+       "      1.635159\n",
+       "      -1.136585\n",
+       "      -0.007239\n",
        "      ...\n",
-       "      0.660882\n",
-       "      1.338977\n",
-       "      -1.475265\n",
-       "      -1.475989\n",
-       "      0.910175\n",
-       "      0.325985\n",
-       "      0.349857\n",
-       "      0.066698\n",
-       "      0.557359\n",
-       "      1.001401\n",
+       "      -2.404202\n",
+       "      -2.074570\n",
+       "      0.022878\n",
+       "      1.345018\n",
+       "      0.705361\n",
+       "      0.414329\n",
+       "      -1.010186\n",
+       "      -0.893686\n",
+       "      -1.028702\n",
+       "      -1.022452\n",
        "    \n",
        "    \n",
        "      2499\n",
-       "      3.783388\n",
-       "      4.814916\n",
-       "      0.656157\n",
+       "      21.658258\n",
+       "      7.675401\n",
+       "      5.660952\n",
        "      1\n",
-       "      3.824017\n",
-       "      -0.870393\n",
-       "      -0.000393\n",
-       "      -0.373005\n",
-       "      -1.857066\n",
-       "      0.327473\n",
+       "      5.741192\n",
+       "      -0.103523\n",
+       "      -1.083828\n",
+       "      0.896827\n",
+       "      0.146243\n",
+       "      1.363973\n",
        "      ...\n",
-       "      0.725508\n",
-       "      -1.219133\n",
-       "      -2.110389\n",
-       "      -0.375057\n",
-       "      0.280377\n",
-       "      -0.899183\n",
-       "      0.315558\n",
-       "      0.841183\n",
-       "      0.194693\n",
-       "      1.001401\n",
+       "      -1.310958\n",
+       "      -0.004224\n",
+       "      0.206620\n",
+       "      0.012787\n",
+       "      -1.777783\n",
+       "      0.340176\n",
+       "      1.907981\n",
+       "      1.581676\n",
+       "      1.832247\n",
+       "      0.977650\n",
        "    \n",
        "  \n",
        "\n",
@@ -349,57 +360,57 @@
        ""
       ],
       "text/plain": [
-       "         Y_cont     Y_bin    T_cont  T_bin     alpha  feature_0  feature_1  \\\n",
-       "0     17.285021  5.560022  4.908333      1  4.221970  -0.711212   0.678770   \n",
-       "1     -5.418737 -1.169369 -1.416456      0  3.571828  -0.971269   0.155694   \n",
-       "2      8.329916  5.750195  1.859907      1  6.389524   0.155810   0.097990   \n",
-       "3    -14.109005 -3.260798 -3.616069      0  1.522008  -1.791197  -0.025364   \n",
-       "4     15.927794  6.070905  4.285630      1  8.262360   0.904944  -0.648125   \n",
-       "...         ...       ...       ...    ...       ...        ...        ...   \n",
-       "2495  -6.973181 -2.492475 -0.493568      1  6.399022   0.159609   2.004918   \n",
-       "2496  -7.457602  1.094568 -2.850723      0  9.043466   1.217386  -1.788360   \n",
-       "2497 -12.389506 -5.563091 -2.275472      0  2.631578  -1.347369   0.948935   \n",
-       "2498   4.181542  1.763852  1.805896      1  6.074689   0.029876   1.265076   \n",
-       "2499   3.783388  4.814916  0.656157      1  3.824017  -0.870393  -0.000393   \n",
+       "         Y_cont     Y_bin    T_cont  T_bin      alpha  feature_0  feature_1  \\\n",
+       "0      3.169837 -0.170346  1.113394      0   9.236102   1.294441   0.418241   \n",
+       "1     10.479049  6.662990  2.272020      1   3.787487  -0.885005   0.315013   \n",
+       "2      7.307821  4.118982  2.062946      1   3.311157  -1.075537   1.058536   \n",
+       "3      9.781360  0.649647  4.043904      1   8.423450   0.969380   0.698199   \n",
+       "4      5.739283  6.812030  0.642417      1   6.613922   0.245569  -0.338491   \n",
+       "...         ...       ...       ...    ...        ...        ...        ...   \n",
+       "2495  -5.099912 -0.870746 -1.409722      0   6.565630   0.226252   1.589653   \n",
+       "2496 -32.742858 -7.337551 -8.468435      0   4.760520  -0.495792   0.546002   \n",
+       "2497   6.759804  1.912040  1.615921      0  10.445934   1.778373   0.097808   \n",
+       "2498 -11.249395 -1.938808 -3.103529      0   5.715321  -0.113872   0.747480   \n",
+       "2499  21.658258  7.675401  5.660952      1   5.741192  -0.103523  -1.083828   \n",
        "\n",
        "      feature_2  feature_3  feature_4  ...  feature_8  feature_9  feature_10  \\\n",
-       "0      0.807216   1.502488  -0.194990  ...  -0.355420   1.402186   -1.517787   \n",
-       "1      0.094208   1.060349   0.918542  ...   0.647043   0.978512    0.102692   \n",
-       "2      1.886364   0.204515   1.407368  ...  -0.789757  -1.376844   -1.164524   \n",
-       "3     -1.849614  -0.420513  -0.813558  ...  -0.967529   0.604905    0.136397   \n",
-       "4     -0.420076   0.774960   0.612525  ...   2.362694   2.702110   -1.565191   \n",
+       "0      0.536286  -0.615573  -1.173784  ...   0.559393   1.111766   -0.216069   \n",
+       "1      0.810138   1.137214   0.203685  ...  -0.017179   0.410818    0.670074   \n",
+       "2      1.908944   1.122914   0.611691  ...   0.050641   1.245489   -1.070642   \n",
+       "3      0.314419   1.446987  -2.729092  ...  -1.052429   1.143079   -1.757701   \n",
+       "4      0.814305  -0.859798   0.334968  ...  -0.547622   0.778724    0.678956   \n",
        "...         ...        ...        ...  ...        ...        ...         ...   \n",
-       "2495   1.850936  -0.205821   1.826866  ...   0.180511  -0.656888   -1.253321   \n",
-       "2496  -0.342299  -0.635556   0.595195  ...  -0.020930  -0.459529    1.528013   \n",
-       "2497   0.733226   0.776689   1.007780  ...   0.394645  -1.223821   -0.281657   \n",
-       "2498  -1.047612  -0.174857  -1.611537  ...   0.660882   1.338977   -1.475265   \n",
-       "2499  -0.373005  -1.857066   0.327473  ...   0.725508  -1.219133   -2.110389   \n",
+       "2495   0.056005  -0.386026  -0.462251  ...   0.987633   0.246870   -0.202917   \n",
+       "2496   0.209072   0.666614   0.400847  ...  -0.798775  -0.616483    0.431552   \n",
+       "2497  -0.807658   0.380358  -0.455391  ...   0.668040   1.471963    0.573966   \n",
+       "2498   1.635159  -1.136585  -0.007239  ...  -2.404202  -2.074570    0.022878   \n",
+       "2499   0.896827   0.146243   1.363973  ...  -1.310958  -0.004224    0.206620   \n",
        "\n",
        "      feature_11  feature_12  feature_13  Y_cont_scaled  Y_bin_scaled  \\\n",
-       "0       0.292296   -0.439312   -0.400863       1.478663      1.030321   \n",
-       "1      -1.338446    1.164631    1.157542      -0.477164     -0.677874   \n",
-       "2      -0.854488   -0.563027   -1.762406       0.707221      1.078595   \n",
-       "3       0.087200   -0.575056   -0.244472      -1.225791     -1.208765   \n",
-       "4       1.689404    1.198610    0.117392       1.361744      1.160004   \n",
+       "0       0.451496   -0.863189    0.319180       0.268475     -0.438362   \n",
+       "1       1.954944    0.888255   -0.506518       0.916637      1.321011   \n",
+       "2      -0.060250   -1.857638    0.806913       0.635421      0.666008   \n",
+       "3      -1.167276   -0.164620    1.687004       0.854768     -0.227239   \n",
+       "4       1.715229   -0.439130   -0.238847       0.496327      1.359385   \n",
        "...          ...         ...         ...            ...           ...   \n",
-       "2495    1.057077   -0.803677   -0.759486      -0.611072     -1.013733   \n",
-       "2496    0.135980   -0.448317   -0.113937      -0.652803     -0.103194   \n",
-       "2497   -0.934772   -0.587933    1.399632      -1.077664     -1.793181   \n",
-       "2498   -1.475989    0.910175    0.325985       0.349857      0.066698   \n",
-       "2499   -0.375057    0.280377   -0.899183       0.315558      0.841183   \n",
+       "2495    0.178579    0.763186    0.527462      -0.464865     -0.618693   \n",
+       "2496    1.238957    0.957759   -0.583051      -2.916171     -2.283696   \n",
+       "2497   -0.288768   -0.861025    0.372657       0.586824      0.097788   \n",
+       "2498    1.345018    0.705361    0.414329      -1.010186     -0.893686   \n",
+       "2499    0.012787   -1.777783    0.340176       1.907981      1.581676   \n",
        "\n",
        "      T_cont_scaled  T_bin_scaled  \n",
-       "0          1.535974      1.001401  \n",
-       "1         -0.459080     -0.998202  \n",
-       "2          0.574396      1.001401  \n",
-       "3         -1.152913     -0.998202  \n",
-       "4          1.339552      1.001401  \n",
+       "0          0.347809     -1.022452  \n",
+       "1          0.726014      0.977650  \n",
+       "2          0.657767      0.977650  \n",
+       "3          1.304402      0.977650  \n",
+       "4          0.194070      0.977650  \n",
        "...             ...           ...  \n",
-       "2495      -0.167970      1.001401  \n",
-       "2496      -0.911497     -0.998202  \n",
-       "2497      -0.730043     -0.998202  \n",
-       "2498       0.557359      1.001401  \n",
-       "2499       0.194693      1.001401  \n",
+       "2495      -0.475800     -1.022452  \n",
+       "2496      -2.779944     -1.022452  \n",
+       "2497       0.511847     -1.022452  \n",
+       "2498      -1.028702     -1.022452  \n",
+       "2499       1.832247      0.977650  \n",
        "\n",
        "[2500 rows x 23 columns]"
       ]
@@ -494,12 +505,47 @@
     "data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e2472e18",
+   "metadata": {},
+   "source": [
+    "CausalPy's  `Variable Selection` module provides a way to encode our uncertainty about variable relevance directly into the prior distribution. Rather than choosing which predictors to include, we specify priors that allow coefficients to be shrunk toward zero (or exactly zero) when the data doesn't support their inclusion. The key insight is that variable selection becomes part of the inference problem rather than a preprocessing step. The module offers two fundamentally different approaches to variable selection, each reflecting a different belief about how sparsity manifests in the world.\n",
+    "\n",
+    "### The Spike-and-Slab: Discrete Choices\n",
+    "\n",
+    "The spike-and-slab prior embodies a binary worldview: each variable either matters or it doesn't. Mathematically, we express this as:\n",
+    "\n",
+    "$$ \\beta_{j} = \\gamma_{j} \\cdot \\beta_{j_\\text{raw}}$$\n",
+    "\n",
+    "such that  \n",
+    "\n",
+    "$$ \\gamma_{j} \\in \\{0, 1\\}$$\n",
+    "\n",
+    "So we have the \"spike\"—the coefficient is exactly zero. When $\\gamma_{j} = 1$, we have the \"slab\" i.e. the coefficient takes on a value from the raw distribution.\n",
+    "This approach appeals to our intuition about many real-world scenarios. Consider a propensity score model predicting whether someone receives a treatment. Some demographic variables might genuinely have no relationship with treatment assignment, while others are strongly predictive. The spike-and-slab says: let's let each variable clearly declare itself as relevant or irrelevant.\n",
+    "\n",
+    "### The Regularised Horseshoe: Continuos Moderation\n",
+    "\n",
+    "The horseshoe prior takes a different philosophical stance. Instead of discrete selection, it says: effects exist on a continuum from negligible to substantial, and we should shrink them proportionally to their signal strength. Small effects get heavily shrunk (possibly to near-zero), while large effects escape shrinkage almost entirely.\n",
+    "\n",
+    "$$ \\beta_{j} = \\tau \\cdot \\lambda_{j} \\cdot \\beta_{j\\text{raw}}$$\n",
+    "\n",
+    "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\lambda_{j}$ is local or specific to each coefficient and regularised so as to ensure finite variance. \n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "806df6ea",
    "metadata": {},
    "source": [
-    "### Hyperparameters for Variable Selection Priors"
+    "### Hyperparameters for Variable Selection Priors\n",
+    "\n",
+    "You can control the behaviour of the variable selection priors through some of the hyperparameters available. For the spike and slab prior, the most important hyperparamers are `temperature`, `pi_alpha`, and `pi_beta`. \n",
+    "\n",
+    "Because our sampler doesn't like discrete variables, we're approximating a bernoulli outcome in our sampling to define the spike and slab. The approximation is governed by the `temperature` parameter. The default value of 0.1 works well in most cases, creating indicators that cluster near 0 or 1 without causing sampling difficulties.\n",
+    "\n",
+    "The selection probability parameters `pi_alpha` and `pi_beta` encode your prior belief about sparsity. With both set to 2 (the default), you're placing a Beta(2,2) prior on π, the overall proportion of selected variables. This is symmetric around 0.5 but slightly concentrated there—you're saying \"I don't know how many variables are relevant, but probably not all of them and probably not none of them.\""
    ]
   },
   {
@@ -510,7 +556,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "
"
       ]
@@ -527,9 +573,17 @@
     "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "3237bb49",
+   "metadata": {},
+   "source": [
+    "We'll now fit two models and estimate the implied treatment effect."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "763ca253",
    "metadata": {},
    "outputs": [
@@ -553,16 +607,18 @@
       "                The coefficients should be interpreted appropriately.\n",
       "  warnings.warn(\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [beta_t, beta_z, chol_cov]\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6de2845a3ae7405c85006a8c991e5614",
+       "model_id": "b332c2837f1849329b3b561a9765f3e5",
        "version_major": 2,
        "version_minor": 0
       },
@@ -577,6 +633,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n"
      ]
@@ -595,8 +657,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 99 seconds.\n",
-      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 133 seconds.\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/experiments/instrumental_variable.py:187: UserWarning: Warning. The treatment variable is not Binary.\n",
       "                This is not necessarily a problem but it violates\n",
@@ -622,16 +683,18 @@
      "output_type": "stream",
      "text": [
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [pi_beta_t, beta_t_raw, gamma_beta_t_u, pi_beta_z, beta_z_raw, gamma_beta_z_u, chol_cov]\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b1fa39df9f8f40b4ae84c1d4783720c9",
+       "model_id": "db312f02efe743c386a4f2b4449f8904",
        "version_major": 2,
        "version_minor": 0
       },
@@ -646,6 +709,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
@@ -666,9 +733,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 461 seconds.\n",
-      "There were 79 divergences after tuning. Increase `target_accept` or reparameterize.\n",
-      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n"
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 551 seconds.\n",
+      "There were 167 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     }
    ],
@@ -676,8 +744,8 @@
     "sample_kwargs = {\n",
     "    \"draws\": 1000,\n",
     "    \"tune\": 2000,\n",
-    "    \"chains\": 2,\n",
-    "    \"cores\": 2,\n",
+    "    \"chains\": 4,\n",
+    "    \"cores\": 4,\n",
     "    \"target_accept\": 0.95,\n",
     "    \"progressbar\": True,\n",
     "    \"random_seed\": 42,\n",
@@ -722,25 +790,588 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2ccb6e0b",
+   "metadata": {},
+   "source": [
+    "The models have quite a distinct structure. Compare the normal IV model with non variable selection priors. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "838e0726",
+   "execution_count": 17,
+   "id": "e97a9ca2",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "clusterinstruments (15)\n",
+       "\n",
+       "instruments (15)\n",
+       "\n",
+       "\n",
+       "clustercovariates (16)\n",
+       "\n",
+       "covariates (16)\n",
+       "\n",
+       "\n",
+       "cluster3\n",
+       "\n",
+       "3\n",
+       "\n",
+       "\n",
+       "cluster2 x 2\n",
+       "\n",
+       "2 x 2\n",
+       "\n",
+       "\n",
+       "cluster2\n",
+       "\n",
+       "2\n",
+       "\n",
+       "\n",
+       "cluster2500\n",
+       "\n",
+       "2500\n",
+       "\n",
+       "\n",
+       "cluster2500 x 2\n",
+       "\n",
+       "2500 x 2\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t\n",
+       "\n",
+       "beta_t\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t\n",
+       "\n",
+       "mu_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t->mu_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z\n",
+       "\n",
+       "beta_z\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y\n",
+       "\n",
+       "mu_y\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z->mu_y\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov\n",
+       "\n",
+       "chol_cov\n",
+       "~\n",
+       "_LKJCholeskyCov\n",
+       "\n",
+       "\n",
+       "\n",
+       "cov\n",
+       "\n",
+       "cov\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->cov\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_corr\n",
+       "\n",
+       "chol_cov_corr\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_corr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_stds\n",
+       "\n",
+       "chol_cov_stds\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_stds\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood\n",
+       "\n",
+       "likelihood\n",
+       "~\n",
+       "Multivariate_normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu\n",
+       "\n",
+       "mu\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 7,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "pm.model_to_graphviz(result_normal.model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34f3a1b7",
+   "metadata": {},
+   "source": [
+    "Now compare the structure of the spike and slab model. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "4f8c2685",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "clusterinstruments (15)\n",
+       "\n",
+       "instruments (15)\n",
+       "\n",
+       "\n",
+       "clustercovariates (16)\n",
+       "\n",
+       "covariates (16)\n",
+       "\n",
+       "\n",
+       "cluster3\n",
+       "\n",
+       "3\n",
+       "\n",
+       "\n",
+       "cluster2 x 2\n",
+       "\n",
+       "2 x 2\n",
+       "\n",
+       "\n",
+       "cluster2\n",
+       "\n",
+       "2\n",
+       "\n",
+       "\n",
+       "cluster2500\n",
+       "\n",
+       "2500\n",
+       "\n",
+       "\n",
+       "cluster2500 x 2\n",
+       "\n",
+       "2500 x 2\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_z\n",
+       "\n",
+       "pi_beta_z\n",
+       "~\n",
+       "Beta\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z\n",
+       "\n",
+       "gamma_beta_z\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_z->gamma_beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_t\n",
+       "\n",
+       "pi_beta_t\n",
+       "~\n",
+       "Beta\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t\n",
+       "\n",
+       "gamma_beta_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "pi_beta_t->gamma_beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t_raw\n",
+       "\n",
+       "beta_t_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t\n",
+       "\n",
+       "beta_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t_raw->beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t\n",
+       "\n",
+       "mu_t\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_t->mu_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t->beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t_u\n",
+       "\n",
+       "gamma_beta_t_u\n",
+       "~\n",
+       "Uniform\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_t_u->gamma_beta_t\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z_u\n",
+       "\n",
+       "gamma_beta_z_u\n",
+       "~\n",
+       "Uniform\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z_u->gamma_beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z\n",
+       "\n",
+       "beta_z\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y\n",
+       "\n",
+       "mu_y\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z->mu_y\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "gamma_beta_z->beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z_raw\n",
+       "\n",
+       "beta_z_raw\n",
+       "~\n",
+       "Normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "beta_z_raw->beta_z\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov\n",
+       "\n",
+       "chol_cov\n",
+       "~\n",
+       "_LKJCholeskyCov\n",
+       "\n",
+       "\n",
+       "\n",
+       "cov\n",
+       "\n",
+       "cov\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->cov\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_corr\n",
+       "\n",
+       "chol_cov_corr\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_corr\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov_stds\n",
+       "\n",
+       "chol_cov_stds\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->chol_cov_stds\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "likelihood\n",
+       "\n",
+       "likelihood\n",
+       "~\n",
+       "Multivariate_normal\n",
+       "\n",
+       "\n",
+       "\n",
+       "chol_cov->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu\n",
+       "\n",
+       "mu\n",
+       "~\n",
+       "Deterministic\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_y->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu_t->mu\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "mu->likelihood\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm.model_to_graphviz(result_spike_slab.model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "368660c8",
+   "metadata": {},
+   "source": [
+    "Despite seeing some divergences in our spike and slab model, most other sampler health metrics seem healthy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0755095c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "az.plot_energy(result_spike_slab.idata, figsize=(20, 6));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5bffd8b6",
+   "metadata": {},
+   "source": [
+    "And since we know the true data generating conditions we can also assess the derived posterior treatment estimates. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "838e0726",
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -752,28 +1383,42 @@
    "source": [
     "fig, ax = plt.subplots(figsize=(20, 6))\n",
     "az.plot_posterior(\n",
-    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    "    result_normal.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    "    label=\"Normal\",\n",
     ")\n",
     "az.plot_posterior(\n",
     "    result_spike_slab.idata,\n",
     "    var_names=[\"beta_z\"],\n",
     "    coords={\"covariates\": [\"T_cont\"]},\n",
     "    ax=ax,\n",
+    "    color=\"green\",\n",
+    "    label=\"spike and slab\",\n",
     ")\n",
-    "ax.axvline(3, color=\"black\", linestyle=\"--\")"
+    "ax.axvline(3, color=\"black\", linestyle=\"--\", label=\"True value\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "057b4f5d",
+   "metadata": {},
+   "source": [
+    "This plot suggests that the spike and slab prior was better able to ignore noise in the process and zero in on the true effect. This will not always work but it is a sensible practice to at least sensitivity check difference between the estimates under different prior settings. We can observe how aggressively the spike and slab prior worked to cull unwanted variables from each model by comparing the values on the coefficients across each model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "id": "127888b7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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fjl69er2yruoD+K1bt7S23bx5EwYGBrCxscnTcfNKNSj95TJVLA8fPsSOHTsQFBSE8ePHq+s8f/4c6enpOtvM67Vxd3fHunXrIITAqVOnEBYWhqlTp8LMzAzjx4+HnZ0dFAoF0tLSNJIYIQRu376tnpThTbVr1w7Lli1DZGSkxjm+KDIyEoaGhuo1TlSvi5cnb8jPh+OyZctCqVTi9u3buSZ9Lz4nXp4E4ubNm+rn7psqjNfRi8zNzdGrVy/MmjULVlZWub6u8vqcF0JAkqRcn68vKlOmDCRJQmxsLExMTLTq6yojIuIdGKJiTrXApUwmU5c9e/YMv/32W77aADQ/LAghCn0KVXNzc7Rq1QqJiYmoU6cOGjZsqPWj+pCkiuXlb+SrVasGNzc3nDx5Uuf+DRs2RKlSpQoU3+DBg+Hg4ICvvvoq14VCIyIi1HE4OTlh7dq1GrO9PXnyBJs3b1bP0lSYwsPDNY6VkpKCI0eOqD+oS5IEIYTWh75ffvkFSqWyUGKQJAl169bFt99+C2traxw/fhwA0KZNGwDA6tWrNepv3rwZT548UW9/U926dUPNmjUxe/Zsra6NALB+/Xrs3bsXQ4YMUX/br+oOderUKY26L84eppLb807VzW/JkiW5xta6dWsA2tcgISEBZ8+eLbRrkJ/XUV599tln8PX1xZQpU7TuCKrk9TlvYWGBxo0bIyIiQuNO4uPHj7F9+3aNNlVrLt24cUPnebi7u+frPIiKGm9v70JfEJZ4B4ao2OvUqRMWLlyIPn36YNiwYbh37x7mz5+fr28u27VrB2NjY/Tu3RtfffUVMjIysGTJkreyYOOiRYvQokULeHp64rPPPoOLiwseP36MS5cuYfv27eqxFJUrV4aZmRnWrFmDGjVqwNLSEuXLl0f58uXx008/4cMPP0SHDh0wcOBAODk5IT09HWfPnsXx48excePGAsVWunRpbN26FZ07d0b9+vU1FrK8ePEiVq9ejZMnT8Lf3x8GBgaYO3cuPv74Y3Tu3BmffPIJnj9/jnnz5uHBgweYPXt2YV42AEBqaiq6deuGoUOH4uHDhwgKCoKpqSkmTJgAALCyskLLli0xb948lClTBi4uLoiJicHy5cthbW1d4OPu2LEDP/74I/z8/FCpUiUIIRAREYEHDx6gXbt2AHKeQx06dMDXX3+NR48eoXnz5jh16hSCgoJQv3599OvXrzAuAWQyGTZv3ox27dqhadOmGDNmDJo2bYrnz59j+/btWLZsGby8vLBgwQL1Pg4ODmjbti1mzZoFGxsbyOVy7N+/X52Mvkj1gXnOnDn48MMPIZPJUKdOHXh6eqJfv36YPn067ty5g86dO8PExASJiYkwNzfH//73P1SrVg3Dhg3D4sWLYWBggA8//BDJycmYPHkyKlSogFGjRhXKNQDy/jrKq3r16uV611YlP8/5adOmoWPHjmjXrh3GjBkDpVKJOXPmwMLCQuNuYPPmzTFs2DAEBgbizz//RMuWLWFhYYFbt27h0KFDcHd3x2effZavcyEqyVTLDbz3i/HqZ+4AIsqL3KZRftmKFStEtWrVhImJiahUqZKYNWuWWL58ucZMUkJozjz0su3bt4u6desKU1NT4eTkJMaNG6eezenF2Zu8vLxErVq1tPbPrW0A4osvvtAou3Llihg0aJBwcnISRkZGomzZsqJZs2Zi+vTpGvXCw8NF9erVhZGRkQAggoKC1NtOnjwpPvroI2Fvby+MjIyEg4ODaN26tVi6dKm6Tl6v38tu374tvv76a1GrVi1hbm4uTExMRJUqVcQnn3wiTp8+rVE3MjJSeHh4CFNTU2FhYSHatGkjDh8+rFFHNQvZi9PoCpEzq5KFhYXW8V++xqpZtH777TcxYsQIUbZsWWFiYiI8PT01ZrESQojr16+L7t27CxsbG1GqVCnRsWNHcebMGSGXy8WAAQPydG1enoXs3Llzonfv3qJy5crCzMxMlC5dWjRu3FiEhYVp7Pfs2TPx9ddfC7lcLoyMjISjo6P47LPPxP379zXq5fZc8fLyEl5eXlrluty9e1eMHz9eVK9eXZiamgpLS0vRuHFj8f3334vMzEyt+rdu3RIBAQHC1tZWlC5dWvTt21c9u9qLs5A9f/5cDBkyRJQtW1ZIkqRxHZRKpfj2229F7dq1hbGxsShdurRo2rSp2L59u3p/pVIp5syZI6pWrSqMjIxEmTJlRN++fdVTgL94ru/qdaTLq/4WqOiaRlmIvD3nhRBi27Ztok6dOsLY2FhUrFhRzJ49W/1aeNmKFSuEh4eHsLCwEGZmZqJy5cqif//+Gs9vzkJGxZFqxsGXX0dvCwCdr7H3jSTESyvdERFRkRIdHY1WrVph48aNWgtpEhFR0eXt7Y2YmBhERUWpu/u+Taou4e/7x3uOgSEiIiIiomKDCQwREREREXLuYKjuYmzevBktW7aEtbU1JElST9FeUOfOnUOPHj1QpkwZmJmZoUGDBtiwYYPOurdv38bixYvRoUMHuLi4wNTUFDY2NvDy8tI5SU9wcLDGrJKq81D9vGnsRQ0H8RMRFXHe3t7vfXcAIqKiZM6cORg/fjzKlSuHqlWrvnEC8Ndff6mTjKpVq+Lq1as4fvw4evbsiczMTPTt21ej/i+//ILJkyfDzMwM5cuXh7u7O1JTU3Hw4EEcPHgQR44c0ZgVsWLFimjevDkOHz4MAOq14lRym12wuOIYGCIiIiIi/DeGxNjYWL0osCRJUCgUAABDw/x9968aA2NkZIRPPvkE8+bNg6mpKYQQmDBhAubMmYPy5cvj6tWrGsshHDp0CFlZWWjZsqVG+alTp9CrVy+cPXsW0dHR8PLy0hn/+/7xnl3IiKjISU5O1rr9bWVlhbp16yI0NLTQ1jR5Ey/frgdy3qjexSDNwvbkyRPMmTMHdevWhZWVFUqVKoXKlSvjo48+QkxMTIHaHDhwoHr9FRVJkjB8+PBCiFj/dJ2fLllZWfjpp5/QqFEj2NrawtzcHHK5HF27dsWWLVvU9VTP+bCwsHzHEh0dDUmSsGnTpnzvS0S6ffLJJxg6dKj677yhoWG+k5cX1axZE4sWLVLfCZEkCdOmTYODgwNu3ryptU5VixYt0KpVK43kBQDq1KmDxYsXAwDWrFlT4HiKO3YhI6Ii63//+x/69OkDAHjw4AG2bduGUaNG4dq1axrrfBQVP/74o75DyDelUon27dvj9OnTGDduHBo3bgwAuHjxIrZv347Y2Fitb/jyYvLkyfjyyy8LO9xip1+/foiIiMDIkSMREhICExMTXL58Gbt378aePXvQrVs3fYdIRDr079+/UNsbNGgQDAw07xsYGRmhbt26uH37Ni5fvoz69etrbH/8+DHWrVuHQ4cO4datW3j27BmEEHj+/DkA4OTJk4UaY3HCBIaIiqyKFSuiSZMm6scdO3bEmTNnEB4eXiQTmJo1a+o7hHxT9aVesWIFAgMD1eUdOnTA8OHDkZ2dXaB2K1euXFghFltXrlzB+vXrMWXKFISEhKjL27Rpg6FDhxb42hLR21ejRo1CbS+3v4n29vYAgH///VejPDExEZ07d8bNmzdzbfPFRWFLGnYhI6JipXTp0jAyMtIoW79+Pdq3bw9HR0eYmZmhRo0aGD9+PJ48eaJR7/Lly+jVqxfKly8PExMTlCtXDm3atMGJEye02mvatCksLCxgaWmJDh06IDEx8bWxvdyFTNUtaP78+Vi4cCFcXV1haWmJpk2b4ujRo1r7//nnn+jSpQtsbW1hamqK+vXr5zpDjUpWVhbs7e11rnT/4MEDmJmZYfTo0bnuf+/ePQCAo6Ojzu0vfmMYFhYGSZKwb98+BAYGwtbWFhYWFvD19cXly5c19stLFyshBL755hsYGRnh559/VpcX9PqnpaXh888/R82aNWFpaQl7e3u0bt0asbGxGvXy+3sJCwtDtWrVYGJigho1amDVqlWvjQXI37XV5dKlSwgMDISbmxvMzc3h5OQEX19fnD59Wmf9jIwMjB49Gg4ODjAzM4OXl1eerhsRabOwsHgn7an+Drw4ZkWpVOKjjz7CzZs34ePjg5iYGNy9excKhQJCCFy8eBFAzt//kooJDBEVWdnZ2VAoFFAoFLh37x5WrFiB3bt3a31Yv3jxInx8fLB8+XLs3r0bI0eOxIYNG+Dr66tRz8fHB3/99Rfmzp2Lffv2YcmSJahfvz4ePHigrjNz5kz07t0bNWvWxIYNG/Dbb7/h8ePH8PT0xD///FOg8/jhhx+wb98+hIaGYs2aNXjy5Al8fHzw8OFDdZ2oqCg0b94cDx48wNKlS7F161bUq1cPPXv2fOW4CCMjI/Tt2xebN2/Go0ePNLaFh4cjIyND487Kyxo2bAgjIyN8+eWXWLNmDW7duvXa8xk8eDAMDAywdu1ahIaG4tixY/D29ta4jq/z/Plz9OnTB99//z22b9+OoUOHAniz66/6NjIoKAg7d+7EypUrUalSJXh7eyM6Olqrfl5+L2FhYQgMDESNGjWwefNmTJo0CdOmTcOBAwdee441atSAtbU1QkJCsGzZsnzPYnTz5k3Y2dlh9uzZ2L17N3744QcYGhrCw8MD58+f16r/zTff4PLly/jll1/wyy+/4ObNm/D29tZKLomoaDt27BguXboEuVyOiIgItGzZEnZ2durxMNeuXdNzhEWAICIqYq5cuSIA6PwZOHCgUCgUue6bnZ0tsrKyRExMjAAgTp48KYQQ4u7duwKACA0NzXXfq1evCkNDQ/G///1Po/zx48fCwcFBfPTRR+qyoKAg8fKfUC8vL+Hl5aV1Hu7u7hoxHzt2TAAQ4eHh6rLq1auL+vXri6ysLI02O3fuLBwdHYVSqcw17lOnTgkAYtmyZRrljRs3Fg0aNMh1P5Xly5cLS0tL9TV2dHQU/fv3FwcPHtSot3LlSgFAdOvWTaP88OHDAoCYPn26umzAgAFCLpdr1AMgvvjiC3Hv3j3RokUL4eTkJE6cOKHenp/rnxcKhUJkZWWJNm3aaMSc19+LUqkU5cuXFx988IHIzs5W10tOThZGRkZa56fLzp07RZkyZdTX1s7OTvTo0UNs27ZNo54qppUrV77yfDIzM4Wbm5sYNWqUujwqKkoAyDXOIUOGvDZOIsqheq0WFi8vLwFAREVF6dw+YMAArdf+2rVrBQDh7++vc59vv/1WAND5N6iw4y+qeAeGiIqsL7/8EgkJCUhISEBUVBRmzpyJDRs2oHfv3hr1Ll++jD59+sDBwQEymQxGRkbqgednz54FANja2qJy5cqYN28eFi5ciMTERK0xCHv27IFCoUD//v3Vd34UCgVMTU3h5eWl81v8vOjUqZPGTDJ16tQBAKSkpADI6Sp07tw5fPzxxwCgcWwfHx/cunVL5zfuKu7u7mjQoAFWrlypLjt79iyOHTuGQYMGvTa+QYMG4fr161i7di1GjBiBChUqYPXq1fDy8sK8efO06qviVGnWrBnkcjmioqJee6wrV66gadOmePToEY4ePYq6deuqtxXG9V+6dCk++OADmJqawtDQEEZGRti/f7/6efCi1/1ezp8/j5s3b6JPnz4aM87J5XI0a9bstbEAOXf9rl69ii1btmDs2LGoVasWIiMj0aVLl9fOyKZQKDBz5kzUrFkTxsbGMDQ0hLGxMS5evKjzfHKLMy+/FyIqOszMzAAAd+7c0dqWlZWF0NDQ1+777NmztxJbUcEEhoiKLGdnZzRs2BANGzaEt7c3JkyYgMmTJ2Pjxo3Ys2cPgJyBj56enoiPj8f06dMRHR2NhIQEREREAPjvj7gkSdi/fz86dOiAuXPn4oMPPkDZsmUxYsQIPH78GMB/bxaNGjWCkZGRxs/69etx9+7dAp2HnZ2dxmMTExON2FTHHTt2rNZxP//8cwB47bEHDRqEuLg4nDt3DgCwcuVKmJiYaCV7uSldujR69+6NRYsWIT4+HqdOnUK5cuUwceJEra5hDg4OWvs7ODiox3y8yrFjx3DhwgX07NkTzs7OGtve9PovXLgQn332GTw8PLB582YcPXoUCQkJ6Nixo84389f9XlTnk9v55pWZmRn8/Pwwb948xMTE4NKlS6hZsyZ++OEH/P3337nuN3r0aEyePBl+fn7Yvn074uPjkZCQgLp16+o8nzf5vRBR0dGkSRMYGhri8OHDGmPuHj58iI8//lhnYqNSqVIlACjwFPjFBWchI6JiRfUt+cmTJ9GhQwccOHAAN2/e1FrQS9d4DLlcjuXLlwMALly4gA0bNiA4OBiZmZlYunQpypQpAwDYtGkT5HL52z+Z/6c67oQJE+Dv76+zTrVq1V7ZRu/evTF69GiEhYVhxowZ+O233+Dn5wcbG5sCxVSrVi306tULoaGhuHDhgnp6ZQC4ffu2Vv3bt2+jSpUqr223Z8+ecHBwwMSJE5GdnY1Jkyapt73p9V+9ejW8vb01VqcGoE5Q80uV4OR2vgVVsWJFDBs2DCNHjsTff/+NWrVq6ay3evVq9O/fHzNnztQov3v3LqytrfMU0+3bt7USNSIq2hwcHDBy5EjMnz8fAwYMwOTJk1G2bFn8888/UCgU+O677/DZZ5/p3Ldnz56YMmUKOnfujDp16sDKygoAsG7dunx98VLUMYEhomJFNWOYaupJVZcZ1bfnKj/99NMr26latSomTZqEzZs34/jx4wBypg42NDREUlISunfvXsiR565atWpwc3PDyZMntT6s5pWNjQ38/PywatUqNG3aFLdv385T97F79+6hVKlSMDY21tqmuptTvnx5jfI1a9ZoXJ8jR44gJSUFQ4YMyVOskyZNQqlSpTBq1Cg8efIEs2bNAvDm11+SJK3nwalTpxAXF4cKFSrku71q1arB0dER4eHhGD16tPq5lpKSgiNHjmhdl5c9fvwYkiTB0tJSa5uqC9ir2tB1Pjt37sSNGzd0Jou5xVnY61kQ0ds3d+5cODs7Y+nSpbh8+TKePn2Ktm3bYuLEiShXrlyu+40fPx5KpRLr1q3DP//8o14zJiMj412F/k4wgSGiIuvq1avqaW2fPHmCuLg4zJo1C3K5XH2nolmzZrCxscGnn36KoKAgGBkZYc2aNVoLfJ06dQrDhw9Hjx494ObmBmNjYxw4cACnTp3C+PHjAQAuLi6YOnUqJk6ciMuXL6Njx46wsbHBnTt3cOzYMVhYWGis51GYfvrpJ3z44Yfo0KEDBg4cCCcnJ6Snp+Ps2bM4fvw4Nm7c+No2Bg0ahPXr12P48OFwdnZG27ZtX7tPVFQUvvzyS3z88cdo1qwZ7OzskJqaivDwcOzevRv9+/fX6ur1559/YsiQIejRoweuXbuGiRMnwsnJSd3dLS++/PJLWFpaYtiwYfj333/x3XffvfH179y5M6ZNm4agoCB4eXnh/PnzmDp1KlxdXaFQKPIcm4qBgQGmTZuGIUOGoFu3bhg6dCgePHiA4ODgPH2Tef78eXTo0AG9evWCl5cXHB0dcf/+fezcuRPLli2Dt7f3K8fSdO7cGWFhYahevTrq1KmDv/76C/PmzdP6faikpqaq43z48CGCgoJgamqKCRMm5PvciUoq8cJ0xoXhdWP3wsLCdM40KUkSvvzyy1wXBM4tTiMjIwQHByM4ODifkRYz+p5FgIjoZbpmITM1NRVVq1YVI0eOFLdu3dKof+TIEdG0aVNhbm4uypYtK4YMGSKOHz+uMbPLnTt3xMCBA0X16tWFhYWFsLS0FHXq1BHffvut1qxmkZGRolWrVsLKykqYmJgIuVwuAgICxB9//KGuk59ZyObNm6d1jgBEUFCQRtnJkyfFRx99JOzt7YWRkZFwcHAQrVu3FkuXLs3TdVMqlaJChQoCgJg4cWKe9rl27ZqYNGmSaN68uXBwcBCGhoaiVKlSwsPDQyxevFjj2qhmIdu7d6/o16+fsLa2FmZmZsLHx0dcvHhRo91XzUL2ovDwcGFoaCgCAwPVM63l5frr8vz5czF27Fjh5OQkTE1NxQcffCAiIyO1Ysnv7+WXX34Rbm5uwtjYWFStWlWsWLFC5/m97P79+2L69OmidevWwsnJSRgbGwsLCwtRr149MX36dPH06VOtmF6ciej+/fti8ODBwt7eXpibm4sWLVqI2NhYreeZahay3377TYwYMUKULVtWmJiYCE9PT/Hnn3++MkYiouJIEqKQU00iInovqdZESUhIQMOGDfUdDhERlVDsQkZERERE9BotWrTIc91BgwblaRwiFQwTGCIiIiKi1zh8+HCe6+ZlDCIVHLuQERERERFRscGFLImIiIiIqNhgFzIqEYQQBV7MjoiI6G0pVaqUeu2e9w3fe6mgXve6YAJDJcLjx49RunRpfYdBRESk4eHDh+rV0t83fO+lgnrd64JjYKhE4LdARERUFPEODJG2170umMAQEREREVGxwUH8RERERERUbDCBISIiIiKiYoMJDBERERERFRuchYyIiAiAUqlEVlaWvsOg94iRkRFkMpm+wyB67zCBISKiEk0Igdu3b+PBgwf6DoXeQ9bW1nBwcHhvZxoj0gcmMEREVKKpkhd7e3uYm5vzgyYVCiEEnj59itTUVACAo6OjniMien8wgSEiohJLqVSqkxc7Ozt9h0PvGTMzMwBAamoq7O3t2Z2MqJBwED8REZVYqjEv5ubmeo6E3leq5xbHVxEVHiYwRERU4rHbGL0tfG4RFT4mMEREREREVGxwDAyVGEqlEtnZ2foOg4iKkKysLAghkJ2dzb8P70B0dDTatGmDe/fuwdraWt/hvBPZ2dkQQiArK0trDIyRkZGeonp3+N5L+ZWX1wUTGCoxrl27hszMTH2HQURFiFKphEKhQFZWFgwMilenhKFDh2L16tWYOnUqxo0bpy7ftm0bevbsiWfPnukxOt0UCgUAIDMzs8T8Pc7KyoJCocD169e1EpiqVavqKap3h++9lF95eV0wgaESIzMzEzKZDIaGfNoTUQ6FQgGFQgFJkorlWAVTU1MsXLgQQ4cOhY2NDYD/xlwU9HwyMzNhbGxcaDHqUlyvd0GoztXY2Fjj/UeVzL3v+N5L+ZHX1wWfTVSiGBoalohb9kSUNy9+kC5uH6glSULr1q2RlJSEefPmYdasWVrbAWDLli0ICQlBUlISHB0d8dlnn2HUqFHqelWrVkVgYCCSkpKwdetWdOnSBV5eXhg7dixWrlyJr7/+GtevX0fHjh2xfPlyREREYNq0aXj48CF69+6NBQsWqO8srF27FosXL8aFCxdgYWEBb29vzJ8/H/b29hoxlaQEBsg5XyMjoxL7IZ7vvVTYSuYriYiISBchgKyn+jm2kTmQzw/1MpkM06ZNQ//+/fHFF1/A2dlZY/vx48fRp08fTJ48GQEBATh69ChGjBgBOzs79O/fX11v4cKFmDBhAsaPHw8AOHLkCJ4+fYoffvgBq1evxuPHj9GzZ0989NFHsLa2xtatW3H58mX06tULzZs3R48ePQDkfNseFBSEqlWrIi0tDePGjcOQIUOwbdu2N7w4RET/YQJDRESkkvUUxvNd9HLozLHJgLFFvvfr2rUr6tati2nTpuGnn37S2LZo0SK0atUK33zzDYCcuy1nz57FwoULNRIYb29vjB49Wv34yJEjyMrKwuLFi1G5cmUAQLdu3bB27Vpcu3YNlpaWqFGjBry8vBAdHa1OYAYOHKhuo1KlSli4cCGaN2+Of//9F5aWlvk+NyIiXYrXiEUiIiLSMmPGDPz22284e/asRvm5c+fQrFkzjbKmTZvi0qVLUCqV6rIGDRpotWlubq5OXgCgXLlykMvlGolIuXLlkJaWpn584sQJdO/eHW5ubrCzs0O7du0A5AzkJiIqLLwDQ0REpGJknnMnRE/HLihPT0+0a9cOkydPRr9+/dTlQgitsSZCCK39VavFa4Tz0pgF1TiOl8tUU+Q+efIEnTp1Qtu2bbFy5UqUKVMG165dQ+fOnTkLFREVKiYwREREKpJUoG5cRcGMGTPQqFEjuLm5qctq1KiBw4cPa9Q7evQo3NzctKb0fVPnz5/H3bt3MX36dFSoUAFAzhgcIqLCxi5kRERE74HatWujd+/e+PHHH9VlI0eORFRUFGbOnIkLFy7gt99+w5IlSzRmISssFSpUgLGxMX788UdcvnwZ27dvx8yZMwv9OERETGCIiIjeE0FBQRpdxOrXr4+1a9diw4YN+OCDDzB16lRMmTJFYwB/YSlbtix++eUXbN68GfXq1cP8+fMxe/bsQj8OEZEkdHWGJXoPXbhwASYmJpyLnojUFAoFHj16hIoVK8LU1FTf4dB7KCMjA1evXoWVlZXGOjBZWVmQy+V6jOzdyOt7r1KpRHx8PFJTU2Fvbw8PD49C7+ZIRV9eXxccA0P0Hrpy7xmuP8yEc2ljuNqZ6TscIiKiXO3atQtBwSG4eeO6uqy8kzNCgoPg4+Ojx8ioqGICQ/QeefBMgaDdyUi49lhd1qhCKYR0dIG1GV/uRERUtOzatQvDhg2DWeVGcOg7EkZl5chKS8GDoxswbNgwLFu2jEkMaeEYGKL3SNDuZCSlZ+CHPh/g2MQ2+KHPB0hKz0DwnmR9h0ZERKRBqVQiKDgEZpUboYz/JJg4VYeBsRlMnKqjjP8kmFVuhKCQqRprFhEBBUhgoqOjIUkSgoOD30I4RVtYWBgkSVL/9OrVS98hlSh9+/bVuP5hYWH6DqlIuXLvGRKuPca0ru7oVMcR9qVM0amOI6Z2rY1jVx/jyr1n+g6RiIhILT4+HjdvXIdVk48gSZofSSXJAFZNeuDm9WuIj4/XU4RUVBW5OzAuLi5wcXHRdxiv1LVrVwQFBSEgIECjfODAgZAkCUePHn2j9ovDNXgbVAlibomJv78/goKC0LVr13cbWDFx/WHOQnGNXG00yhu72mpsJyIiKgpSU1MBAEZldQ/aNioj16hHpFLkEpjiwM/PD8HBwVoJDL1d/v7+CA4Ohp+fn75DKZKcSxsDABKu3NcoP3YlXWM7ERFRUWBvbw8AyEpL0bk9626KRj0iFSYwRO8JVzszNKpQCpO3nsaOUzeR+jgDO07dxJStZ9C4YinORkZEREWKh4cHyjs549HRDRAiW2ObENl4dHQjyjtXgIeHh54ipKLqjRKYgwcPwsvLC5aWlrC1tUWfPn1w/fp1rXqpqakYNWoUqlSpAhMTE5QpUwbdu3fHmTNn1HWSk5MhSRJSUlKQkpKiMdZBNd4mMzMTixcvRocOHVChQgWYmJjA3t4e/v7+SExMLNA5qI77qp/k5OQCtf1i+wMHDsTly5cREBAAGxsbWFhYoG3btjh58mS+roHKwYMH4evrizJlysDExARubm6YNGkSnj59qlHvxTFLcXFx6NChA6ytrSFJkrqOEAK//vorWrZsCWtra5ibm8PNzQ2ffvoprl69qtHe48ePERQUhFq1asHMzAzW1tbo2LEjDh06pHXu3t7ekCQJGRkZ+Oqrr1ChQgWYmprC3d0dK1as0Kg7cOBABAYGAgACAwM1zp3yLqSjC6rYmWL42kQ0nrEfw9cmooqdKYI7uOg7NCIiIg0ymQwhwUF4lpSAuxHT8fzGWWQ/f4rnN87ibsR0PEtKQEjQFK4HQ1oKPK/q0aNHMWvWLHTq1AkjRozA8ePHER4ejkOHDiEhIQHlypUDACQlJcHb2xs3btxA+/bt4efnh9TUVGzevBl79uzB/v374eHhAWtrawQFBSE0NBQAMHLkSPWxvL29AQDp6ekYOXIkPD094ePjAxsbG1y+fBnbtm3D77//joMHD6JRo0b5Og/VcV/27NkzLFy4ENnZ2YWyuFlycjI8PDxQs2ZNDBo0CElJSdi6dStatWqFs2fPoly5cnm6BgCwdOlSfP7557CxsYGvry/Kli2LhIQEzJgxA1FRUYiKioKxsWZ3oSNHjmDmzJlo1aoVhg0bpk5MhBDo3bs31q9fDycnJ/Tu3RtWVlZITk7G+vXr0bFjR1SsWBFAzvVv2bIl/v77b3h6eqJDhw54+PCh+jw2btyos3tXjx49cOrUKfTo0QNZWVnYsGEDBg8ejDt37mDChAkAcrrlPXjwAFu3bkXXrl1Rr169N77mJZG1mSFC/apwHRgiIioWfHx8sGzZspx1YFaPU5eXd66ARZxCmXJR4ARmz549+OWXXzB48GB12dSpUxEUFIRvvvkGy5cvBwD0798ft2/fxp49e9CuXTt13UmTJqFhw4YYOnQoTp06BWtrawQHB6sHcOua5czGxgZXr16Fk5OTRvnff/+NJk2a4JtvvsG+ffvydR6q475ICIFevXpBoVBg3rx5cHBwyFebusTExGD27Nn4+uuv1WWTJ0/G9OnTsXLlSowfPz5P1+Cff/7B//73P9SrVw9//PEHbG1t1dtmz56NCRMmYPHixRgzZozGfvv27cPy5csxaNAgjfIlS5Zg/fr1aNOmDbZv3w4zs/8+7D579gzPnv03c9X//vc//P3331ixYoX6bgkAzJw5E40aNcKwYcPQsWNHrYTv8uXLOHPmDEqVKgUAmDhxIj744ANMmTIFPXv2RKVKlTQSGD8/PwwcODBvF5Z0crUzY+JCRK+UnJyMatWq4dixY6hbty5iYmLQvn173LlzB9bW1voO75VWrVqFsWPHvtHg7oK0UbVqVQwfPhwjRowo8HFJm4+PDzp06ID4+HikpqbC3t4eHh4evPNCuSpwF7Jq1appfRgeN24cypYti/DwcGRmZiIxMRFHjhzBgAEDNJIXIOePwNChQ3H69GmNrmSvYmJiopW8AECtWrXQqlUrHDx4EFlZWQU9JbWgoCBs2LABgYGBGDt27Bu3BwCurq4YN26cRpkq+UtISMhzOz/99BMUCgW+++47jeQFAL766iv19X9Z/fr1tX5fAPDDDz9AJpNhyZIlGskLAJiZmamPcffuXXWi82LyAgDlypXDuHHjkJaWhj/++EPrGBMnTlQnLwDg4OCA0aNHQ6FQYO3atXk+dyIiypGamorPP/8cVapUQalSpVCxYkV06tQpX7NgVqhQASkpKahVq9ZbjFR/oqOj0b59ezg4OMDa2ho1a9bE4MGDoVAo9B0a6SCTydCsWTP4+fmhWbNmTF7olQp8B6Z58+Za4xPMzMzQoEED7N69GxcuXFD/Ib19+7bOuwnnzp1T/1u7du08HffEiROYO3cuDh06hNu3b2slLHfv3oWjo2MBzihHeHg4pk2bBk9PTyxdurTA7bysbt26MDDQzBednZ0BAA8ePMhzO6prunv3bp3JgpGRkfq6vqhx48ZaZU+ePME///yDKlWqwM3N7ZXHTUhIgFKpREZGhs7f5cWLFwHk/C47d+6ssc3T01OrvqrsxIkTrzwuERFp69WrF7KysvDLL7/A1dUVqampiIqKQnp6ep7bkMlkhdLDoCj6559/0KVLF3zxxRf49ttvYWpqikuXLmHLli3Izs5+fQP0zimVSt6BoTwrcAKT25R2qrEvDx8+VP8h3blzJ3bu3JlrW0+ePMnTMY8cOYLWrVsDANq3bw83NzdYWlpCkiRERkbi5MmTeP78eX5OQ8PRo0cxaNAgVKpUCREREVrjSN5E6dKltcoMDXMuf35WmFVd0xkzZuTr+Krfy4tUiZOuu1q5Hffw4cM4fPhwrvV0/S51PVdefJ5Q4TC8fwmyh1ehLF0RCpsq+g6HqFgSQuBZln4+4JoZGeRp4pIHDx7g8OHD2LdvH1q2bAkAkMvlWmNATUxM8N1332HHjh04ePAgypUrh1mzZqF79+4AtLuQvezZs2fo1asX0tPTsXXrVtja2uLXX3/FggULkJycDLlcji+++AKffvpprrHu2bMHs2fPxt9//w2ZTAYPDw8sWLAAlStX1ohh/fr1+PHHH3Hs2DFUqVIF33//PZo0aaJuZ9WqVZg6dSru3r2Ldu3aoVmzZq+8Rn/88QccHBwwa9YsdVnlypXRoUOHXPdJSkrCV199hWPHjuHJkyeoXr06pk2bhjZt2mjU+/fff9G/f3/s2LEDVlZWGDduHL744otXxkOvtmvXLkwNnoJrN26pyyo4OWJK8FSOgSGdCpzA5NZn9M6dOwByPrBbWVkBABYvXozhw4cX9FBqM2bMwPPnz3Ho0CE0b95cY9vRo0c1ZvTKr6tXr8LPzw/GxsbYvn07ypQp86bhvhWqa/ro0SONblmvo+tNUZVU3bhxI8/HHTNmDObPn5/n4wI5z5UKFSpolL34PKE3Y5BxHzb7voTJrf+6Ij53bIT77RYh29TmFXsS0cueZWWj3vQDejn2iUmtYW78+m+cLS0tYWlpiW3btsHDwwMmJia51g0JCcH06dOxYMECrF27Fv369UPNmjVRo0aNVx7j4cOH6NatG0xMTLB7925YWFhg+fLlmDZtGkJDQ1G3bl2cPHkSn332GSwsLNCvXz+d7Tx9+hRffvklatWqhadPnyIkJAQfffQREhISNHolTJkyBbNnz0aVKlUQFBSE/v37459//oGhoSGOHTuGYcOGYdq0aejatSv27t2LadOmvTJ+BwcH3L59G7GxsTp7Aejy5MkTdOzYESEhITAxMcHq1avh7++P06dPqyezAYCFCxfi66+/xqRJk7Bv3z6MGzcO1apVQ9u2bfN0HNK0a9cuDBs2DJ3dDLFhsDlq28twJlWJmbF3MWzYMCzjQH7SocBjYA4fPgwhhEbZs2fP8Ndff8HMzAxVq1ZVz9sdFxeX53ZlMlmudySSkpJga2urlbw8ffoUx48fz+cZ/Offf/+Fr6+vepxHzZo1C9xWYXjVNVBd0/z0c86NpaUlatasiStXrqi7gOWmUaNGkCQpX79LldjY2FzLXpxtTHWrOD93pEoiKeupxo/NH6Ngcv8i0CMMGHMB6BEGk/sXYfPHaK26UtbT17ZPREWboaEhfvnlF6xevRr29vbw9vbG5MmTcfr0aa26/v7+GDRoEKpWrYrg4GA0aNAAP/744yvbv3PnDtq2bYuyZcti69atsLCwAADMmjULc+bMgZ+fH1xdXeHn54cRI0bgl19+ybWtbt26wc/PD25ubqhbty5++uknnDlzBmfPntWoN2rUKPj4+KBq1aqYMmUKUlJScOnSJQDA999/j3bt2mHcuHHqQfQvj6t9Wffu3dGzZ0+0bdsWcrkcPXr0wI8//ohHjx7luk+dOnUwdOhQ1K5dG25ubggJCYGrqyt27NihUa9p06bqWL744gv4+/vju+++e2U8pJtSqcTU4Cno7GaIyF6maOJsCEtjCU2ccx53djPEtJAgfi4gLQW+A3P+/HmsWLFCYxayefPmIS0tDYMGDYKxsTEaN24MDw8PhIeHo0uXLujZs6dGG9nZ2YiNjYWXl5e6zNbWFmfOnEFGRobWbFZyuRwXLlzA33//rR50qFQqMXbsWKSlpRXoPLKzs9GnTx+cOnUKixYtQseOHQvUTmF61TX4/PPP8fPPP+N///sf9u3bp3Vn48GDB7hy5Qrq16+fp2N98cUX+OKLL/D5559j27ZtGgP5MzIy8PTpU9ja2sLBwQEfffQR1q9fj3nz5mHs2LFad3Xi4+Ph7u4Oc3NzjfIZM2bA19dXfcfozp07WLhwIQwNDdGnTx+N8wagcy0h+o/jygbahT3CgFrdcv5fqxsgBEw2Beqse3PYWa0yIsphZmSAE5Na6+3YedWtWzd8+OGHOHToEOLj47F3714sWLAAS5cuRf/+/dX1XuyGBeR8CXbq1KlXtu3j44MGDRpg7dq16i+W0tLScO3aNXzyySf47LPP1HUVCsUr76QnJSUhJCQE8fHxuHfvnnr8ydWrVzUmD3B3d1f/XzUuJy0tDdWrV8e5c+fQtWtXrfPYu3dvrseVyWT4+eefERwcjOjoaMTHx2POnDlYsGABDh06pHOs7JMnTzB9+nTs2rULt27dgkKhwLNnz3Dt2jWNerqu6eLFi3ONhXIXHx+PazduYcNgcxi89JnCQJIwoYURmq24ifj4+Nd2G6SSpcAJTPv27fH5559j586dqF69Oo4fP449e/agQoUKmDlzprpeeHg4WrVqhV69eiE0NBQNGjSAqakprl69iri4OKSlpSEjI0Ndv3Xr1vjzzz/h6+sLT09PGBsbo0WLFmjRogX+97//Ye/evWjRogU++ugjmJqaIjo6Gjdu3IC3tzeio6PzfR6bNm3C9u3b4ejoiPT0dJ0D1EeOHPlOp5R81TWoXbs2fvzxR3z22WeoVq0afHx8ULlyZTx69AiXL19GTEwMBg4cmOcJCD777DPExMRgw4YNcHNzQ5cuXWBlZYWrV69iz549WL58uXptlx9//BHnz5/HV199hd9++w1NmzZF6dKlce3aNfz111+4ePEibt26pZXAVKpUCbVr10b37t3V68CkpqZixowZqFSpkrpe06ZNYWZmhtDQUDx69Ahly5YFAIwfP75wLuz7rOJLf9jlzXXXI6JXkiQpT924igJTU1O0bdsWbdu2xcSJE/Hpp59i2rRpGgmMLq8bZ9OxY0dERkbi7Nmz6gl2VInHkiVLtMbavGqgtb+/P5ydnbFkyRI4OjpCCIH69esjMzNTo56RkZFWfKpjvtzbIz+cnJzw8ccf4+OPP0ZISAhq166Nn3/+GVOmTNGqO378eOzbtw9z5sxB5cqVYWpqit69e2vFqgsXXS4Y1XCE2va6n0Oq8jeZLpveTwVOYJo2bYqJEydi0qRJWLRoEYyNjdGrVy/MnTtXY8C4q6srEhMTsXDhQkRGRmLFihWQyWRwdHREy5YtERAQoNHu5MmTcf/+fezYsQMHDhxAdnY2goKC0KJFC3Tu3BmbNm3CzJkzsXr1apibm6N169bYsmULpk6dWqDzUK1cf+vWLYSEhOisM3DgwHeawLzqGgDA0KFDUa9ePSxcuBAHDx7Etm3bULp0aVSsWBGjRo3CgAED8nwsSZKwbt06tG/fHr/88gtWrVoFIQScnJzw0UcfoUGD/77Bt7W1xZEjR/D9999j/fr1WLNmDbKzs+Hg4IC6deti8uTJOscObdiwAVOmTEF4eDjS0tLg5uaGmTNnaty9U7W/adMmBAcHY8mSJeo1aJjAaLoV+Jf6/4YPLqPslh7A1SP/3YEBgJSciRbSum2EwrrSy00Q0XuoevXq2LZtm0ZZfHw8+vbtq36c24D9F82YMQOWlpbo2LEj9u3bhxo1aqBcuXJwcnLClStX0Lt37zzFc+/ePZw7dw4//PCD+v3rVZPA5KZGjRqIj4/XKDt27Fi+27GxsYGDg0OuEwcdPnwY/fv3V9/t+ffff5GSkqJVT1cs1apVy3c89N8kP2dSlWjirP2R9EyqUqMekUq+Exhvb2+Nb0MOHjz42n1sbGwwbdq01w66A3LGZSxbtizX7d27d1fPoPKisLAw9QKQ+TFw4MBCWzRRVwwuLi6v/PZI17bXXQMgZ0yKrvVeXvby70sXSZIwePBgrYRCFzMzM4wbN05rTZvX7TNv3jzMmzfvtXV9fHw4WO81hNF/d7iyytbGc6dmMN45BpIQOXdeUg5D7BqLTOdmyCqbt+nJiaj4uHfvHvr06YMBAwbA3d0dlpaWOH78OBYuXAhfX1+NuhEREWjQoAGaNWuGdevWISEhAT/99NNrjzFnzhwolUp06NABe/fuRfXq1TFp0iSMHj0apUqVQocOHZCZmYm//voL9+/fx8iRI7XasLGxgZ2dHZYvXw4HBwdcu3YNkyZNyvf5fvHFF/Dy8sL8+fPRpUsX/PHHH6/sPgYAP//8M06ePImuXbuiUqVKeP78OVavXo1//vkH3377rc59KleujMjISHTq1AmSJCE4OFjnlMtxcXHqWPbv34/NmzcjMjIy3+dFOd3vKjg5YmbsXUT2kml0I8sWArMOZaGic3n1+F8ilQIP4i/JAgMDIUkSevXqpe9QSpS+fftCkiSthTRLuvtt5iPTthqwKRBYUBXYFIhM22q43zp/s8URUfFgaWmJRo0a4bvvvkObNm3wwQcfIDg4GIMGDUJoaKhG3cmTJ2PDhg1o2LAhVq9ejV9//fW1M5CpzJ8/H927d0fHjh1x4cIFDBo0CEuXLsVvv/2GBg0aoG3btvjtt9/g6uqqc38DAwP89ttvOH78OD744AOMGzdOY1rjvPLw8MDSpUuxZMkSNG7cGH/88cdr78w3atQIT548wfDhw1G/fn20bdsWx44dw8aNG9VTT79s3rx5sLGxgZeXF/z9/dGuXTud40lHjhyJxMREeHh4qCc2aN++fb7Pi3K6H04JnoodFxXwW5eBuGsKPH4uEHct5/GOiwpMDgrhejCkRRJv0rm0hDlx4oTGtyy1a9fW6gJH//H29kZMTMwb9V9+UUREhMbgUz8/P41ZzF7nwoULMDEx0ehr/T7hOjBE+adQKPDo0SNUrFhRa9KU4s7ExAQbNmzQGgBP71ZGRgauXr0KKysr9fpvAJCVlQW5XK7HyN6NvLz36loHpqJzeUwOCmGvjBImr6+LAo+BKcqSk5Pz1J3M2tpa523v3NSrVy9fH5ipcPn7+8Pf31/fYRRZCpsqTFyIiKjY8fHxQYcOHRAfH4/U1FTY29vDw8ODd14oV+9tApPbgPwXyeXyfCUwlD8FmRWOiIiISh6ZTMapkinP3ssEJi8D14mIiN5nz58/13cIRERvBQfxExERERFRscEEhoiISjTesae3jc8xosLFBIaIiEosA4Oct0HVwrlEhU313FI914jozb2XY2CIiIjywsDAAMbGxkhLSwOQs/AuUWF59uwZ0tLSYGxszASGqBAxgaESRaFQ6DsEIipiTExMkJ2djTt37kB6YSVwojclhICRkRFMTEyQlZWlsa0kvR+VpHOlN5PX5woTGCoxjI2NkZmZCaVSqe9QiKiIkclkMDAwgBCC4xWoUEiSpP7JzMzUdzh6w/deehskwb/UVEIolUpkZ2frOwwiIiIAeOXq9O8LvvdSfuXldcEEhoiIiIiIig2OKCMiIiIiomKDCQwRERERERUbTGCIiIiIiKjYYAJDRERERETFBhMYIiIiIiIqNrgODBERERHpnVKpRGxsLG7dugVHR0d4enpCJpPpOywqgpjAEBERFUEX7zxG8r2ncLEzh1u5UvoOh+itioiIwMhRo3Htaoq6rEJFOUK/XQh/f389RkZFEdeBISIiesfu/fs81233n2Zi4pYziL+Sri7zcLXFjG61YWNunOdj2FmavFGMRO9KREQEAgICYFa5EayafASjsnJkpaXg0dENeJaUgE2bNjGJIQ1MYIiIiPLpaabijfavOWVPrtsMJKCUqRFmdnNHI1cbJFy5j2+2nMbjjCxk5+Md+5+pHd4oxrfB3JgdP0iTUqmEa6XKuGdcDmX8J0GS/hueLUQ27kZMh11WKq4kXWJ3MlJjAlOEhIWFITAwUP24Z8+eWLdunR4jKjouXboENzc39WO5XI7k5GT9BUREJZrL+J1vtf0f+nyATnUc1Y93nLqJ4WsT3+ox34Xk2Z30HQIVMdHR0WjVqhUc+s6HiVN1re3Pb5zF7dXjEBUVBW9v73cfIBVJnIWsCOratSuCgoIQEBCA4OBgSJKU55/g4GB9h19gLi4ucHFx0bnN1tYWQUFBCAoKQunSpd9tYERE71gjVxuNx41dbfUUCdHbdevWLQCAUVm5zu1GZeQa9YgADuIvkvz8/DBw4EAAQJkyZbS2R0dHIyYmBl27dkW9evU0tr2v307Y2tqqk7OwsDC9xkJE9Kbds17VhQwAEq7c17gDc+yF8TB5VRS7kBG9zNEx53melZai8w5M1t0UjXpEABOYIs/b21srKQkODkZMTIxGokNERO/Om47l+GtS21y3fb7mOCZvPQMBgcautjh2JR1Ttv4ND1db/PjxB+8sRqJ3wdPTExUqynHv6AadY2AeHd2ICnIXeHp66jFKKmrYhYyQmpqKsWPHolq1ajA1NYWtrS2aNGmCBQsWaNXdsWMHWrVqhdKlS8PMzAz16tVDaGgolEqlRr3k5GRIkoSBAwfi8uXLCAgIgI2NDSwsLNC2bVucPHlSq25KSgpSUlLemy5xRES5sbM0yfVnSd8GqFXeCsPXJqLxjP0YvjYRtcpbYUnfBq/c7+UfouJAJpMh9NuFeJaUgLsR0/H8xllkP3+K5zfO4m7EdDxLSkDowgUcwE8a+PVMCXfx4kW0atUKN27cQIsWLeDn54cnT57gzJkzmDFjBsaMGaOuu2jRIowcORK2trbo06cPLCwssH37dowaNQqxsbHYtGkTJEnSaD85ORkeHh6oWbMmBg0ahKSkJGzduhWtWrXC2bNnUa5cOVhbWyMoKAihoaEAgJEjR6r3f1+7xBER5cbWwhi/DfbgOjBUYvj7+2PTpk0568CsHqcuryB3QSinUCYdmMCUcH379sWNGzewbNkyDB06VGPb9evX1f+/fPkyxo4dC3t7e/z555+oUKECAGDmzJlo3749IiIisGbNGvTt21ejjZiYGMyePRtff/21umzy5MmYPn06Vq5cifHjx8Pa2hrBwcHqsS2860JEBLiVK8XEhUoMf39/dO3aFbGxsbh16xYcHR3h6enJOy+kE7uQlWAJCQk4duwYWrZsqZW8AICzs7P6/2vWrIFCocCYMWPUyQsAGBsbY/bs2QB0D653dXXFuHHjNMoGDx6sPj4RERERkNOdzNvbG71794a3tzeTF8oV78CUYMeOHQMAtG/f/rV1ExNz1h/Q1aWrSZMmMDMzw4kTJ7S21a1bFwYGmnmyKjF68OBB/gImIiKi95JSqeTdF8oz3oEpwVQJhJOT02vrPnr0CABQrlw5ndvt7e3x8OFDrXJda7YYGubkzS8P/CciomIq9RxwblfOv0T5FBERgSqVXNCqVSv06dMHrVq1QpVKLoiIiNB3aFRE8Q5MCWZtbQ0AuHHjxmvrWllZAQDu3LkDuVx7sanU1FR1HSIiKgae3H3zNp6mAztGAimH/yuTNwc6hwLmb7D4poX2Gmj0foqIiEBAQAA6uxkifLA5atvLcCZViZmH7iAgIACbOIifdGACU4I1btwYALB3715MnDjxlXXr16+PLVu2IDo6Wr2fyrFjx/Ds2TM0bdr0jeKRyWTIzMx8ozaIiIqczCf6jkC3eZXfvA3JADCxAnqEARWbAVePANtHAj96ACK74O1+c/PNY3tbjC30HcF7Q6lUYsyoL9HZzRCRvUxh8P8zmTZxNkRkTxn81mdg7OiR6Nq1K7uTkQYmMCVYo0aN0LhxYxw8eBA///yz1kD+GzduqLuX9enTB1OnTsXChQvRt29flC9fHgCQlZWF8ePHA8AbL6ppa2uLM2fOICMjA6ampm/UFhFRkTGzvL4jeHtENuAbCtTqlvO4VjdACGBT4Ju1W5SvWbB2d2kqmNjYWCRfvY7wwebq5EXFQJIwobkRmq24htjYWC6rQBqYwJRwq1evhre3N4YNG4bffvsNTZs2RUZGBv7++28kJibi3r17AIDKlStjzpw5GDNmDOrUqYOPPvoIFhYW2LFjB86dO4euXbtqTaGcX61bt8aff/4JX19feHp6wtjYGC1atECLFi0K41SJiOhtqNhM87G8uX7ioGLn1q1bAIDa9rrvrqjKVfWIVJjAlHBubm44fvw4Zs2ahe3btyM0NBSWlpZwc3PDpEmTNOqOHj0aVapUwcKFC7F69WpkZmaiatWqWLBgAUaMGKG1iGV+TZ48Gffv38eOHTtw4MABZGdnIygoiAkMERVvRbU7VGHd5bh65L87MIDmeJiCKqrXjAqVo6MjAOBMqhJNnLU/kp5JVWrUI1KRhBBC30FQjrCwMAQGBmLlypVv3B3rfebi4gIASE5O1mscRETFWmEM4t/QH0g7D/jMy7nzknIY2DUOKFsd+OjXgrfLQfwlglKpRJVKLnA3u4PInqYa3ciyhYDf+gycyXDAxaQrHANDGpjAFCGqBEalZ8+eWLdunR4jKjouXboENzc39WO5XM4EhohI357cAzYPBi5H/VdWqRXQfTlgYae/uKjYUM9CVtUQE5obqWchm3U4CzsuKDgLGenEBKYIOXHiBCIjI9WPa9eujYCAAP0FVISkp6fju+++Uz+2trbGyJEj9RcQERH9J/UckH4ZsK0E2FfXdzRUzERERGDMqC+RfPW6usxVXgHzF4YyeSGdmMC8ZyIjI3HixInX1vP29uaMHkRERFQkKJVKxMbG4tatW3B0dISnpye7jVGuOIj/PRMZGYlff81bv2MmMERERFQUyGQyfi6hPOMdGCIiIiIiKjYM9B0AERERERFRXjGBISIiIiKiYoMJDBERERERFRtMYIiIiIiIqNjgLGRUYiiVSmRnZ+s7DCIiIgCAkZGRvkN46/jeS/mVl9cFExgqMa5du4bMzEx9h0FERAQAqFq1qr5DeOv43kv5lZfXBRMYKjEyMzMhk8lgaMinPRER6ZdCodB3CO8E33spP/L6uuCziUoUQ0PDEnHLnoiIqKjgey8VNg7iJyIiIiKiYoMJDBERERERFRtMYIiIiIiIqNhgAkNERERERMUGExgiIiIiIio2mMAQEREREVGxwWmUiYiIiEjvlEol4uPjkZqaCnt7e3h4eEAmk+k7LCqCmMAQ0Vt35d4zXH+YCefSxnC1M9N3OEREVMTs2rULQcEhuHnjurqsvJMzQoKD4OPjo8fIqChiAkNEBXb/adYrtz/MUGBe1HUk3vhXXVbfyRLjWjmjtKnmnx8bcy5yRkRUEu3atQvDhg2DWeVGcOg7EkZl5chKS8GDoxswbNgwLFu2jEkMaZCEEELfQRRVYWFhCAwMVD/u2bMn1q1bp34cHx+PCRMm4OTJk0hPT4eXlxeio6P1EOn7T6FQaK3im9+n7oULF2BiYlJiVgN+lqV868dos+TUK7cbSEApUyPM7OaORq42SLhyH99sOY3HGVnIfunXt/+zOvk6tpkRuxUQUfGVlZUFuVyu7zDeute99yqVSjRp2gwPzMqjjP8kSNJ/w7OFyMbdiOmwzriFo0cOsztZCZDX1wXvwORB165dUa9ePdSuXVtd9vDhQ/j6+iIzMxP9+vWDnZ0dXFxc3kk8kiS9N8nS2rVrERoair///hvGxsZo2rQppk6dioYNG2rUMzAwQFBQEICcxDIlJUUf4RYrr0su3oVsAczs5o5OdRwBAJ3qOEJAYPjaRK26+Y33yIj6hRIjERHpT3x8PG7euA6HviM1khcAkCQDWDXpgZurxyE+Ph7NmjXTU5RU1DCByQM/Pz8MHDhQoywhIQFpaWmYNWsWxo8fr5/AirmZM2di4sSJqFixIj799FP8+++/WLduHZo3b449e/bA29tbXdfAwADBwcEAgOjoaCYwxUgjVxuNx41dbfUUCRERFTWpqakAAKOyur91Nyoj16hHBDCBKbCbN28CABwcHPQcSfF08eJFBAUFoWrVqjh27BhKly4NABgxYgQaN26MIUOG4Ny5czA05FO0oPLbJasg8nLXJOHKffUdGAA4diVdZ713ES8RERUt9vb2AICstBSYOFXX2p51N0WjHhHAdWAKRJIkDBgwAAAQGBgISZIgSZJGl67U1FSMGjUKVapUgYmJCcqUKYPu3bvjzJkzWu1FRUVh0KBBqFatGiwtLWFpaYmGDRti2bJlGvWio6MhSRIAICYmRn1cSZIQFhYGAAgODtaKRSUsLEyjLgAkJydDkiQMHDgQ586dg7+/P8qUKQNJkpCcnKyut3XrVrRp0wY2NjYwNTVF7dq1MX/+fCiVBRtnsXLlSigUCkycOFGdvABArVq10L9/fyQlJeHAgQMFaptymBnJ3vrPziG1X/lT38kSk7eexo5TN5H6OAM7Tt3ElK1nUN/JUqtufo9NRETFn4eHB8o7OePR0Q0QIltjmxDZeHR0I8o7V4CHh4eeIqSiiF9vF0BQUBBOnDiBrVu3qsfHAFCPgUlKSoK3tzdu3LiB9u3bw8/PD6mpqdi8eTP27NmD/fv3a7wQ58yZg0uXLqFJkybo1q0bHjx4gN27d+OTTz7B+fPnsWDBAnX7QUFBCAkJgVwu1+jWpoqhoFTHr1WrFgYMGID09HQYGxsDAL755hvMmjULzs7O6N69O6ysrHDw4EGMG5fTJ3Xjxo35Pp4qwWrfvr3Wtg4dOmDp0qWIiYnRuZ2KjtfNHDbDxxXBe5I1xrw0rlgKwR1cYG3GPz9ERCWdTCZDSHAQhg0bhrsR02HVpAeMysiRdTcFj45uxLOkBCxatowD+EkDP0EUQHBwMMLCwrB161ad42P69++P27dvY8+ePWjXrp26fNKkSWjYsCGGDh2KU6f+63qzZMkSuLq6arShUCjg4+ODRYsW4csvv0TFihXh4uKC4OBghISEqP9fWA4fPozJkydj6tSpGuX79u3DrFmz8OGHH2LTpk0wNzcHkDMD2Oeff46lS5di8+bN6N69e76Od/HiRVhaWursgufm5qauQ8WbtZkhQv2qcB0YIiLKlY+PD5YtW5azDszqcery8s4VsIhTKJMO7EJWyBITE3HkyBEMGDBAI3kBgKpVq2Lo0KE4ffq0Rleyl5MXADA0NMSnn34KpVKJqKiotx63g4MDJk2apFX+/fffAwB++ukndfIC5HSjmz17NiRJQnh4eL6P9/DhQ42uYy+ysrJS16H3g6udGTwrlWbyQkREOvn4+OBo3BFs3LgRP/zwAzZu3IijRw4zeSGdeAemkB09ehQAcPv2bZ13SM6dO6f+VzUt8+PHjzF//nxERkYiKSkJT5480dhHNWHA21S3bl11l7EXHT16FBYWFli+fLnO/czMzNTnRERERFRQMpmMUyVTnjCBKWTp6TkzLO3cuRM7d+7MtZ4qScnMzIS3tzeOHz+O+vXrq9eUMTQ0RHJyMn799Vc8f/78rcddrlw5neXp6elQKBQICQnJdd+XE668KF26dK53WB49eqSuQ0RERO8/pVKJ+Ph4pKamwt7eHh4eHhz3QrliAlPIVN2fFi9ejOHDh7+2/tatW3H8+HEMGTIEP//8s8a2devW4ddff83X8Q0McnoFKhQKrW2v6pKlmt3sZVZWVpAkCXfv3s1XHK/j5uaGuLg43L59W2scjGrsi2osDBUOw/uXIHt4FcrSFaGwqaLvcIiIiAAAu3btwtTgKbh245a6rIKTI6YET2UXMtKJY2AKmWp2sbi4uDzVT0pKAgB06dJFa1tsbKzOfQwMDHKdvtjGJmfRwBs3bmhtS0zUXv38dTw8PHDv3r1CH1Dv5eUFANi7d6/Wtj179mjUIU0Gz9Lz9SO7fxl22/vDfqMv7PZ+kfPv9v6Q3b+c5zaIiIjehl27dmHYsGGoZ3EXcYPN8XhCKcQNNkc9i7sYNmwYdu3ape8QqQjiHZhC1rhxY3h4eCA8PBxdunRBz549NbZnZ2cjNjZW/eFcLs9ZYfbQoUPw9fVV14uJidG6I6Nia2uL69ev69zWsGFDAMCqVavQr18/9R2ZuLg4rFmzJt/nM2LECPz+++8YNGgQIiMjYWdnp7H99u3buH//PmrUqJGvdgMDAzF//nzMmDEDXbt2VXcX+/vvv7Fq1SpUrlwZrVu3zne874qU9VRvx3b4rXn+dpAMABMroEcYULEZcPUITLaPRLlNvsBLc+7n5lbgX/kP9A0II/PXVyIiomJNqVRiavAUdHYzRGQvUxj8f2+QJs6GiOwlg9+6DEwLCUKHDh3YnYw0MIF5C8LDw9GqVSv06tULoaGhaNCgAUxNTXH16lXExcUhLS0NGRkZAABfX1+4uLhg7ty5OHPmDGrXro3z589jx44d8PPzw+bNm7Xab926NTZs2ICAgADUr18fMpkMnTp1gru7O5o0aYKmTZviwIEDaNq0KVq2bImUlBRs27YNvr6+2LJlS77OpWPHjpg8eTKmTZuGKlWqoGPHjpDL5bh37x4uXbqE2NhYTJ8+Pd8JTNWqVREcHIxJkyahTp06CAgIwJMnTxAeHo6srCz8/PPPMDQsuk9Px5UN9B1C3olswDcUqNUt53GtboAQwKbAPDfxrs/35rCz7/R4RET07sXHx+PajVvYMNhcnbyoGEgSJrQwQrMVNxEfH8/B/aSh6H5CLMZcXV2RmJiIhQsXIjIyEitWrIBMJoOjoyNatmyJgIAAdV1LS0scOHAA48aNw8GDBxEdHY1atWphzZo1KFeunM4EZtGiRQCAAwcOYMuWLcjOzoaDgwPc3d0hSRK2bduG0aNHY+fOnTh9+jTq1q2Lbdu24ebNm/lOYABg6tSpaNmyJb777jvs378fDx48gJ2dHVxdXREcHIyPP/64QNdp4sSJcHFxQWhoKJYsWQJjY2M0a9YMU6dORaNGjQrUJuWi4kt/+OX5vItDRERUyFJTUwEAte11311RlavqEalIQgih7yCKqrCwMAQGBmLlypVai1WS/nh7eyMmJgb5fepeuHABJiYmMDJ69erxeaHPLmQFuhvSI+y/OzAAcCYiX3dg2IWMiKhwZWVlqbuRv89e9d575MgR9OjRA3GDzdHEWfs79bhrCjRb8RQbN27kHZgSIq+vC96ByYPAwEAEBgaiZ8+eWLdunb7DKZEUCkWhJB6FRZ8fsG/3O5yv+jZ/jITxzjGQhMi585JyGGLXWGQ6Nsb9tt/mqQ0mFEREVNg8PDxQwckRM2PvIrKXTKMbWbYQmHUoCxWdy6snSCJSYQLzCvXq1UNQUJD6sWrhSXr3DAwMNH4XJVm2mW2+6t9vtwg2B8bC5IU7LpnOzXC/9Xxkm9oUdnhERER5IpPJMCV4KoYNGwa/dRmY0MIIte1lOJOqxKxDWdhxUYFly0I4gJ+0sAsZFYrk5GSEhYW9tp61tTVGjhz51uPRpTC7kBVHXAeGiKjoYBey/+haB6aic3lMDgrhOjAlTF5fF0xgqFBER0ejVatWr60nl8uRnJz89gPSoaQnMEREVHQwgdGkVCoRHx+P1NRU2Nvbw8PDg3deSiCOgaF3ytvbO9+D6omIiIiAnO5kHKhPeWWg7wCIiIiIiIjyigkMEREREREVG0xgiIiIiIio2GACQ0RERERExQYTGCIiIiIiKjY4CxmVKAqFQt8hEBERlaj3o5J0rvRm8vpcYQJDJYaxsTEyMzOhVCr1HQoREVGJwPdeehu4kCWVGEqlEtnZ2foOg4iICABKxMLKfO+l/MrL64IJDBERERERFRscxE9ERERERMUGExgiIiIiIio2mMAQEREREVGxwQSGiIiIiIiKDSYwRERERERUbHAdGCIiIiLSO6VSidjYWNy6dQuOjo7w9PSETCbTd1hUBDGBIdKzi3ceI/neU7jYmcOtXCl9h0NERPTORUREYOSo0bh2NUVdVqGiHKHfLoS/v78eI6OiiOvAEOnJpdTHmLjlDOKvpKvLPFxtMaNbbdiYG8PO0kSP0REREb0bERERCAgIgFnlRrBq8hGMysqRlZaCR0c34FlSAjZt2sQkhjQwgSF6R55mKjQe1w7ag1KmRpjZzR2NXG2QcOU+vtlyGo8zspAtgH+mdnij45kb8wYrEREVbUqlEq6VKuOecTmU8Z8ESfpveLYQ2bgbMR12Wam4knSJ3clIjQnMK4SFhSEwMFD9uGfPnli3bp36cXx8PCZMmICTJ08iPT0dXl5eiI6O1kOk7z+FQgEjIyONsuL21HUZv1Or7Ic+H6BTHUf14x2nbmL42sRCOV7y7E6F0g4REdHbEh0djVatWsGh73yYOFXX2v78xlncXj0OUVFR8Pb2fvcBUpHEr2jzoGvXrqhXrx5q166tLnv48CF8fX2RmZmJfv36wc7ODi4uLu8kHkmSin2y9PTpUyxZsgR//fUXjh8/jgsXLkAIgStXrui8jgYGBggKCgKQk1impKRo1SmOGrnaaDxu7Gqrp0iIiIjevVu3bgEAjMrKdW43KiPXqEcEMIHJEz8/PwwcOFCjLCEhAWlpaZg1axbGjx+vn8CKsdTUVIwdOxYAIJfLYWNjg/T09FzrGxgYIDg4GEDOtzXFMYF5sUvYpdR/0eX7w0i4cl/jDsyxF8bDvGkXMiIioqLO0THnPTArLUXnHZisuyka9YgArgNTYDdv3gQAODg46DmS4qlMmTLYu3cv7t27h+TkZDRq1EjfIb115saG6p86ztbwcLXF5K1nsOPUTaQ+zsCOUzcxZevf8HC1xV+T2mrUL8gPERFRUefp6YkKFeV4dHQDhMjW2CZENh4d3YgKchd4enrqKUIqipjAFIAkSRgwYAAAIDAwEJIkQZIkjS5dqampGDVqFKpUqQITExOUKVMG3bt3x5kzZ7Tai4qKwqBBg1CtWjVYWlrC0tISDRs2xLJlyzTqRUdHQ5IkAEBMTIz6uJIkISwsDAAQHBysFYtKWFiYRl0ASE5OhiRJGDhwIM6dOwd/f3+UKVMGkiQhOTlZXW/r1q1o06YNbGxsYGpqitq1a2P+/PlQKpUFuoaWlpZo164dbG1LbpepJX0boFZ5Kwxfm4jGM/Zj+NpE1CpvhSV9G3AGMiIiKhFkMhlCv12IZ0kJuBsxHc9vnEX286d4fuMs7kZMx7OkBIQuXMAB/KSBX9MWQFBQEE6cOIGtW7eqx8cAUI/dSEpKgre3N27cuIH27dvDz88Pqamp2Lx5M/bs2YP9+/fDw8ND3d6cOXNw6dIlNGnSBN26dcODBw+we/dufPLJJzh//jwWLFigbj8oKAghISGQy+Ua3dpUMRSU6vi1atXCgAEDkJ6eDmNjYwDAN998g1mzZsHZ2Rndu3eHlZUVDh48iHHjxiE+Ph4bN258o2OXVLYWxvhtsAfXgSEiohLN398fmzZtylkHZvU4dXkFuQtCOYUy6SIoVytXrhQAxMqVK/O1rVmzZsLQ0FDs3btXo/z8+fOiVKlSwt3dXaP88uXLWm1kZWWJdu3aCZlMJlJSUjS2ARBeXl46Yw4KChIARFRUVJ5ivnLligAgAIjJkydr7bN3714BQHz44YfiyZMn6vLs7Gzx6aefCgBi06ZNOmPJjw4dOggA4sqVK6+t6+XlJfjUJSIier8oFAoRFRUl1q5dK6KiooRCodB3SFREsQtZIUtMTMSRI0cwYMAAtGvXTmNb1apVMXToUJw+fVqjK5mrq6tWO4aGhvj000+hVCoRFRX11uN2cHDApEmTtMq///57AMBPP/0Ec3NzdbkkSZg9ezYkSUJ4ePhbj4+IiIjebzKZDN7e3ujduze8vb3ZbYxyxS5khezo0aMAgNu3b6tnzXrRuXPn1P+qpmV+/Pgx5s+fj8jISCQlJeHJkyca+6gmDHib6tatq+4y9qKjR4/CwsICy5cv17mfmZmZ+pyIiIiICkKpVCI2Nha3bt2Co6MjPD09mcBQrpjAFDLVVMA7d+7Ezp3aCxeqqJKUzMxMeHt74/jx46hfv756TRlDQ0MkJyfj119/xfPnz9963OXKldNZnp6eDoVCgZCQkFz3fTnhojxKPQekXwZsKwH22lNHEhERlQQREREYM+pLJF+9ri5zqeiMBd8u4vgX0okJTCGzsrICACxevBjDhw9/bf2tW7fi+PHjGDJkCH7++WeNbevWrcOvv/6ar+MbGOT0ClQoFFrbHj58mOt+qtnNXmZlZQVJknD37t18xUG5eHIXeJoO7BgJpBz+r1zeHOgcCpStqq/IiIiI3rmIiAgEBASgs5shwgebo7a9DGdSlZh56A4CAgKwiYP4SQeOgSlkqtnF4uLi8lQ/KSkJANClSxetbbGxsTr3MTAwyHX6YhubnJXdb9y4obUtMTExTzG9yMPDA/fu3cPFixfzvW+JlPnk1T/zKgM/egB3/gZ6hAFjLuT8e+fvnPLX7U9ERPSeUCqVGDPqS3R2M0RkL1M0cTaEpbGEJs6GiOxpis5VDTF29MgCL9lA7y/egSlkjRs3hoeHB8LDw9GlSxf07NlTY3t2djZiY2Ph5eUFIGcVegA4dOgQfH191fViYmK07sio2Nra4vr16zq3NWzYEACwatUq9OvXT31HJi4uDmvWrMn3+YwYMQK///47Bg0ahMjISNjZ2Wlsv337Nu7fv48aNWrku+330szyr68jsgHfUKBWt5zHtboBQgCbAl+/f3Dud9GIiIiKk9jYWCRfvY7wweYweKkniIEkYUJzIzRbcQ2xsbHw9vbWT5BUJDGBeQvCw8PRqlUr9OrVC6GhoWjQoAFMTU1x9epVxMXFIS0tDRkZGQAAX19fuLi4YO7cuThz5gxq166N8+fPY8eOHfDz88PmzZu12m/dujU2bNiAgIAA1K9fHzKZDJ06dYK7uzuaNGmCpk2b4sCBA2jatClatmyJlJQUbNu2Db6+vtiyZUu+zqVjx46YPHkypk2bhipVqqBjx46Qy+W4d+8eLl26hNjYWEyfPr1ACczYsWPVXdNOnz6tLrO0tAQAjB8/HtWrv6djQyo203wsb66fOIiIiPTk1q1bAIDa9roH66vKVfWIVJjAvAWurq5ITEzEwoULERkZiRUrVkAmk8HR0REtW7ZEQECAuq6lpSUOHDiAcePG4eDBg4iOjkatWrWwZs0alCtXTmcCs2jRIgDAgQMHsGXLFmRnZ8PBwQHu7u6QJAnbtm3D6NGjsXPnTpw+fRp169bFtm3bcPPmzXwnMAAwdepUtGzZEt999x3279+PBw8ewM7ODq6urggODsbHH39coOu0adMmpKSkaJS9eL4DBw4sfgnMN6+ZMU51h+Xqkf/uwAD/jYcZGs1xMEREVCI4OjoCAM6kKtHEWfsj6ZlUpUY9IhVJCCH0HURRFRYWhsDAQKxcuVJj1XvSL29vb8TExKBYPnWf3AU29AfSzgM+83LuvKQcBnaNA8pWBwJzn7mOiIjofaJUKlGlkgvcze4gsqepRjeybCHgtz4DZzIccDHpCqdUJg0cxJ8HgYGBkCQJvXr10ncoJZZCoYAkSZAkCTExMfoOp+AsygAf/QY4uOeMeVlQNedfB3fgo1X6jo6IiOidkclkWPDtIuy4oIDf+gzEXVPg8XOBuGs5j3dcUGD+wlAmL6SFXcheoV69eggKClI/Vi08Se+egYGBxu+iWLOwA/pHch0YIiIq8fz9/bFp0yaMGfUlmq34b4IiV3kFbNoUyimUSSd2IaNCkZycjLCwsNfWs7a2xsiRI996PERERFR8KJVKxMbG4tatW3B0dISnpyfvvFCumMBQoYiOjkarVq1eW08ulyM5OfntB0RERERE7yUmMEREREREVGxwED8RERERERUbTGCIiIiIiKjYYAJDRERERETFBhMYIiIiIiIqNrgODJUYSqUS2dnZ+g6DiIgIAGBkZKTvEN46vvdSfuXldcEEhkqMa9euITMzU99hEBERAQCqVq2q7xDeOr73Un7l5XXBBIZKjMzMTMhkMhga8mlPRET6pVAo9B3CO8H3XsqPvL4u+GyiEsXQ0LBE3LInIiIqKvjeS4WNg/iJiIiIiKjYYAJDRERERETFBhMYIiIiIiIqNpjAEBERERFRscEEhoiIiIiIig3OQkZEREREeqdUKhEfH4/U1FTY29vDw8MDMplM32FREcQEhug9dOXeM1x/mAnn0sZwtTPTdzhERESvtGvXLgQFh+DmjevqsvJOzggJDoKPj48eI6OiiAkM0XvkwTMFgnYnI+HaY3VZfSdLjGvljNKmr36525hzjn4iInr3du3ahWHDhsGsciM49B0Jo7JyZKWl4MHRDRg2bBiWLVvGJIY0SEIIoe8giN6FCxcuwMTE5L1eTOvLLZeQlJ6BaV3d0cjVBglX7uObLafxOCML2a95pe//rE6+j2dmxFv7REQFkZWVBblcru8w3rrXvfcqlUo0adoMD8zKo4z/JEjSf8OzhcjG3YjpsM64haNHDrM7WQmQ19cF78C8QlhYGAIDA9WPe/bsiXXr1qkfx8fHY8KECTh58iTS09Ph5eWF6OhoPUT6/lMoFFp//Jh7a7py7xkSrj3GD30+QKc6jgCATnUcISAwfG3ia/dvs+RUvo95ZET9fO9DRESkEh8fj5s3rsOh70iN5AUAJMkAVk164ObqcYiPj0ezZs30FCUVNUxg8qBr166oV68eateurS57+PAhfH19kZmZiX79+sHOzg4uLi7vJB5Jkop9snTixAls3rwZ+/btw+XLl/Hw4UM4OTmhY8eOmDhxIpycnDTqGxgYICgoCEBOYpmSkqKPsIu06w8zAQCNXG00yhu72uojHCIiotdKTU0FABiV1f2tu1EZuUY9IoAJTJ74+flh4MCBGmUJCQlIS0vDrFmzMH78eP0EVox9+umnOHbsGBo1aoRevXrBxMQE8fHxWLJkCTZu3IjY2FhUr15dXd/AwADBwcEAgOjoaCYwOjiXNgYAJFy5r74DAwDHrqTnaf+CdCEjIiJ6E/b29gCArLQUmDhV19qedTdFox4RwASmwG7evAkAcHBw0HMkxVPfvn2xZs0aVK5cWaN8zpw5GD9+PMaMGYOdO3fqKbriydXODI0qlMLkrachINDY1RbHrqRjytYzqO9kiekfurxyf45nISKid83DwwPlnZzx4OgGnWNgHh3diPLOFeDh4aHHKKmo4UKWBSBJEgYMGAAACAwMhCRJkCRJo0tXamoqRo0ahSpVqsDExARlypRB9+7dcebMGa32oqKiMGjQIFSrVg2WlpawtLREw4YNsWzZMo160dHRkCQJABATE6M+riRJCAsLAwAEBwdrxaISFhamURcAkpOTIUkSBg4ciHPnzsHf3x9lypSBJElITk5W19u6dSvatGkDGxsbmJqaonbt2pg/fz6USmWBruHw4cO1khcAGDt2LMzNzRETE1Ogdku6kI4uqGJniuFrE9F4xn4MX5uIKnammOHjChtzo1f+EBERvWsymQwhwUF4lpSAuxHT8fzGWWQ/f4rnN87ibsR0PEtKQEjQFA7gJw28A1MAQUFBOHHiBLZu3aoeHwNAPQYmKSkJ3t7euHHjBtq3bw8/Pz+kpqZi8+bN2LNnD/bv36/xTcKcOXNw6dIlNGnSBN26dcODBw+we/dufPLJJzh//jwWLFigbj8oKAghISGQy+Ua3dpUMRSU6vi1atXCgAEDkJ6eDmPjnC5J33zzDWbNmgVnZ2d0794dVlZWOHjwIMaNyxlUt3Hjxjc69oskSYJMJoOBAXPrgrA2M0SoXxWuA0NERMWGj48Pli1blrMOzOpx6vLyzhWwiFMokw5MYAogODgYYWFh2Lp1q87xMf3798ft27exZ88etGvXTl0+adIkNGzYEEOHDsWpU//N+LRkyRK4urpqtKFQKODj44NFixbhyy+/RMWKFeHi4oLg4GCEhISo/19YDh8+jMmTJ2Pq1Kka5fv27cOsWbPw4YcfYtOmTTA3NweQMwPY559/jqVLl2Lz5s3o3r17ocSxadMmPH78GD169CiU9koqVzszJi5ERFRs+Pj4oEOHDoiPj0dqairs7e3h4eHBOy+kE7/mLmSJiYk4cuQIBgwYoJG8AEDVqlUxdOhQnD59WqMr2cvJCwAYGhri008/hVKpRFRU1FuP28HBAZMmTdIq//777wEAP/30kzp5AXLulMyePRuSJCE8PLxQYrh27RpGjBgBMzMzTJs2rVDaJCIiouJBJpOhWbNm8PPzQ7NmzZi8UK54B6aQHT16FABw+/ZtnXdIzp07p/5XNS3z48ePMX/+fERGRiIpKQlPnjzR2Ec1YcDbVLduXXWXsRcdPXoUFhYWWL58uc79zMzM1Of0JtLT0+Hj44PU1FSsWrUK1apVe+M2iYiIqHhQKpW8+0J5xgSmkKWn50xZu3PnzlfOoqVKUjIzM+Ht7Y3jx4+jfv366jVlDA0NkZycjF9//RXPnz9/63GXK1dOZ3l6ejoUCgVCQkJy3fflhCu/7t+/j7Zt2+Lvv//GkiVL0Ldv3zdqj94ew/uXIHt4FcrSFaGwqaLvcIiI6D2wa9cuTA2egms3bqnLKjg5YkrwVI5/IZ2YwBQyKysrAMDixYsxfPjw19bfunUrjh8/jiFDhuDnn3/W2LZu3Tr8+uuv+Tq+avC7QqHQ2vbw4cNc91PNbvYyKysrSJKEu3fv5iuOvEpPT0fbtm2RmJiIH374AZ988slbOQ7lj8EzzbVjpIwHsD4UDJNbCeqy546N8KBFMISptbos24yLZhIRUd7t2rULw4YNQ2c3Q2wYbI7a9jKcSVViZuxdDBs2DMs4iJ90YAJTyFSzi8XFxeUpgUlKSgIAdOnSRWtbbGyszn0MDAxynb7YxiZnFfYbN25obUtMTHxtPC/z8PDA77//josXL8LNzS3f+7/Ki8nL4sWL8fnnnxdq+yWZlPX0jfZ3+K35Sw0aACZWQI8woGIz4OoRmGwfiXKbfAGRra52K/CvAh1PGJm/vhIREb1XlEolpgZPQWc3Q0T2MoXB/3+Z2sTZEJG9ZPBbl4FpIUHo0KEDu5ORBiYwhaxx48bw8PBAeHg4unTpgp49e2psz87ORmxsLLy8vAAAcrkcAHDo0CH4+vqq68XExGjdkVGxtbXF9evXdW5r2LAhAGDVqlXo16+f+o5MXFwc1qxZk+/zGTFiBH7//XcMGjQIkZGRsLOz09h++/Zt3L9/HzVq1MhXu+np6WjTpg1OnDiBRYsW5SnZo7xzXNmgcBsU2YBvKFCrW87jWt0AIYBNgYVy3JvDzr5hgEREVNzEx8fj2o1b2DDYXJ28qBhIEia0MEKzFTcRHx+PZs2a6SlKKoqYwLwF4eHhaNWqFXr16oXQ0FA0aNAApqamuHr1KuLi4pCWloaMjAwAgK+vL1xcXDB37lycOXMGtWvXxvnz57Fjxw74+flh8+bNWu23bt0aGzZsQEBAAOrXrw+ZTIZOnTrB3d0dTZo0QdOmTXHgwAE0bdoULVu2REpKCrZt2wZfX19s2bIlX+fSsWNHTJ48GdOmTUOVKlXQsWNHyOVy3Lt3D5cuXUJsbCymT5+e7wTG398fJ06cQPXq1ZGenq5zwoORI0fC2to6X+3SW1TxpTcPeXPd9YiIiPIgNTUVAFDbXvfdFVW5qh6RChOYt8DV1RWJiYlYuHAhIiMjsWLFCshkMjg6OqJly5YICAhQ17W0tMSBAwcwbtw4HDx4ENHR0ahVqxbWrFmDcuXK6UxgFi1aBAA4cOAAtmzZguzsbDg4OMDd3R2SJGHbtm0YPXo0du7cidOnT6Nu3brYtm0bbt68me8EBgCmTp2Kli1b4rvvvsP+/fvx4MED2NnZwdXVFcHBwfj444/z3WZycjKAnNnYcpsgYODAgUxgCqigXblUdN5JuXrkvzswAJByuNCPS0REJYe9vT0A4EyqEk2ctT+SnklVatQjUpGEEELfQRRVYWFhCAwMxMqVK7UWqyT98fb2RkxMDPL71L1w4QJMTExgZGT0liJ7f7w8iN/mj5EwfngZks/8nDsvKYchdo1FZunKuN/2W3U9DuInIsqbrKwsdTfy99mr3nuVSiWaN/VAPYu7GmNgACBbCPity8DJp2Vx6MhRjoEpIfL6uuBClnkQGBgISZLQq1cvfYdSYikUCkiSBEmSEBMTo+9w3nvZZrYaP/fbLUKmbbWcMS8LqgKbApFpWw3324Vq1CMiIsormUyGKcFTseOiAn7rMhB3TYHHzwXiruU83nFRgclBIUxeSAu7kL1CvXr1EBQUpH6sWniS3j0DAwON3wW9W9mmNrjns5zrwBARUaHy8fHBsmXLMDV4Cpqt+G8dmIrO5bFsWQinUCad2IWMCkVycjLCwsJeW8/a2hojR4586/Howi5kRERUVLALmSalUon4+HikpqbC3t4eHh4evPNSAuX1dcE7MFQokpOTcx2M/yK5XK63BIaIiIiKJplMxqmSKc+YwFCh8Pb2zvegeiIiIiKi/OIgfiIiIiIiKjaYwBARERERUbHBBIaIiIiIiIoNJjBERERERFRsMIEhIiIiIqJig7OQUYmiUCj0HQIREVGJej8qSedKbyavzxUmMFRiGBsbIzMzE0qlUt+hEBERlQh876W3QRJcvINKCKVSiezsbH2HQUREBACvXZ3+fcD3XsqvvLwumMAQEREREVGxwUH8RERERERUbDCBISIiIiKiYoMJDBERERERFRtMYIiIiIiIqNhgAkNERERERMUG14EhIiIiIr1SKpWIjY3FrVu34OjoCE9PT8hkMn2HRUUUExii99DFO4+RfO8pXOzM4VaulL7DISIiylVERARGjhqNa1dT1GUVKsoR+u1C+Pv76zEyKqq4DgzReyT9SSZGhCfi0KW76rIWVcrgu971YWthrMfIiIiItEVERCAgIABmlRvBqslHMCorR1ZaCh4d3YBnSQnYtGkTkxjSwgSGqBh4mqnIU73BYX/iwp3HmNq1Nhq52iDhyn1M2XoG1RxK4ZcBDQEA5sa88UpERPqnVCrhWqky7hmXQxn/SZCk/4ZmC5GNuxHTYZeViitJl9idjDQwgXmFsLAwBAYGqh/37NkT69atUz+Oj4/HhAkTcPLkSaSnp8PLywvR0dF6iPT9p1AoYGRkpFFWkp66LuN35rnuD30+QKc6jurHO07dxPC1ierHybM7FWpsREREBREdHY1WrVrBoe98mDhV19r+/MZZ3F49DlFRUfD29n73AVKRxa9i86Br166oV68eateurS57+PAhfH19kZmZiX79+sHOzg4uLi7vJB5Jkop9snTy5EmEhobir7/+wo0bN/DkyRM4OTmhQYMG+Oqrr9CwYUON+gYGBggKCgKQk1impKToapYANHK10Xjc2NVWT5EQERHl7tatWwAAo7JynduNysg16hGpMIHJAz8/PwwcOFCjLCEhAWlpaZg1axbGjx+vn8CKsYSEBOzatQtNmzaFl5cXLCwscPnyZWzfvh2bNm3CqlWr0LdvX3V9AwMDBAcHA8j5xqakJTD/TO3w2jqXUv9Fl+8PI+HKfY07MMeupAMAtg1vjir2lm8tRiIiovxwdMx5r8pKS9F5BybrbopGPSIVJjAFdPPmTQCAg4ODniMpnvr27YshQ4Zolf/9999o2LAhxowZg48//hiSJOkhuqInL+NW6jhbo0WVMpiy9QwEBBq72uLYlXQEbf0bnm5lUMfZ+u0HSkRElEeenp6oUFGOe0c36BwD8+joRlSQu8DT01OPUVJRxIUsC0CSJAwYMAAAEBgYCEmSIEmSRpeu1NRUjBo1ClWqVIGJiQnKlCmD7t2748yZM1rtRUVFYdCgQahWrRosLS1haWmJhg0bYtmyZRr1oqOj1R/oY2Ji1MeVJAlhYWEAgODgYK1YVMLCwjTqAkBycjIkScLAgQNx7tw5+Pv7o0yZMpAkCcnJyep6W7duRZs2bWBjYwNTU1PUrl0b8+fPh1KpLNA1NDU11Vleq1Yt1KhRA6mpqXj06FGB2i7JvutdHzXLW2H42kQ0nrEfw9cmomZ5KyzqVV/foREREWmQyWQI/XYhniUl4G7EdDy/cRbZz5/i+Y2zuBsxHc+SEhC6cAEH8JMW3oEpgKCgIJw4cQJbt25Vj48BoB4Dk5SUBG9vb9y4cQPt27eHn58fUlNTsXnzZuzZswf79++Hh4eHur05c+bg0qVLaNKkCbp164YHDx5g9+7d+OSTT3D+/HksWLBA3X5QUBBCQkIgl8s1urWpYigo1fFr1aqFAQMGID09HcbGOdPufvPNN5g1axacnZ3RvXt3WFlZ4eDBgxg3bhzi4+OxcePGNzr2i5KSknD+/HlUqFABpUuXLrR2SwpbC2P8NtiD68AQEVGx4O/vj02bNuWsA7N6nLq8gtwFoZxCmXIjKFcrV64UAMTKlSvzta1Zs2bC0NBQ7N27V6P8/PnzolSpUsLd3V2j/PLly1ptZGVliXbt2gmZTCZSUlI0tgEQXl5eOmMOCgoSAERUVFSeYr5y5YoAIACIyZMna+2zd+9eAUB8+OGH4smTJ+ry7Oxs8emnnwoAYtOmTTpjyYvExEQRFBQkvvnmG/Hxxx+LUqVKCXNzc7Fz585c9/Hy8hJ86hIREb0/FAqFiIqKEmvXrhVRUVFCoVDoOyQqwtiFrJAlJibiyJEjGDBgANq1a6exrWrVqhg6dChOnz6t0ZXM1dVVqx1DQ0N8+umnUCqViIqKeutxOzg4YNKkSVrl33//PQDgp59+grm5ubpckiTMnj0bkiQhPDy8wMc9ceIEQkJCMHPmTKxZswbm5ubYsmULfHx8CtwmERERFS8ymQze3t7o3bs3vL292W2MXoldyArZ0aNHAQC3b99Wz5r1onPnzqn/VU3L/PjxY8yfPx+RkZFISkrCkydPNPZRTRjwNtWtW1fdZexFR48ehYWFBZYvX65zPzMzM/U5FcTAgQMxcOBAZGRk4OLFi1iwYAE+/PBDzJkzB2PHji1wu0RERFR8KJVKxMbG4tatW3B0dISnpyeTGMoVE5hClp6eM2Xtzp07sXNn7osPqpKUzMxMeHt74/jx46hfv756TRlDQ0MkJyfj119/xfPnz9963OXKldNZnp6eDoVCgZCQkFz3fTnhKghTU1O4u7sjLCwMaWlp+Prrr9GxY0eNtXcIQOo5IP0yYFsJsNeecpKIiKi4iYiIwJhRXyL56nV1mUtFZyz4dhHHwJBOTGAKmZWVFQBg8eLFGD58+Gvrb926FcePH8eQIUPw888/a2xbt24dfv3113wd38Agp1egQqHQ2vbw4cNc98ttumIrKytIkoS7d+/mK4430b59e+zatQuxsbElO4F58sI1f5oO7BgJpBz+r0zeHOgcCpi/sFClRZl3FR0REdEbi4iIQEBAADq7GSJ8sDlq28twJlWJmYfuICAgAJs4kJ90YAJTyFSzi8XFxeUpgUlKSgIAdOnSRWtbbGyszn0MDAxynb7YxiZnFfYbN25obUtMTHxtPC/z8PDA77//josXL8LNzS3f+xeEqsucoeF78vTMLOAdqnmV//u/ZACYWAE9woCKzYCrR4DtI4EfPQCR/V+9b/LZ3dDYomCxERERvSGlUokxo75EZzdDRPYyhcH/f5naxNkQkT1l8FufgbGjR6Jr167sTkYa3pNPiEVH48aN4eHhgfDwcHTp0gU9e/bU2J6dnY3Y2Fh4eXkBAORyOQDg0KFD8PX1VdeLiYnRuiOjYmtri+vXr+vc1rBhQwDAqlWr0K9fP/Udmbi4OKxZsybf5zNixAj8/vvvGDRoECIjI2FnZ6ex/fbt27h//z5q1KiRr3YPHz4MDw8PrSTlxIkTWLp0KQwNDbUmQSi2ZpZ/8zZENuAbCtTqlvO4VjdACGBT4JsdKzj3u3JERERvU2xsLJKvXkf4YHN18qJiIEmY0NwIzVZcQ2xsLLy9vfUTJBVJTGDegvDwcLRq1Qq9evVCaGgoGjRoAFNTU1y9ehVxcXFIS0tDRkYGAMDX1xcuLi6YO3cuzpw5g9q1a+P8+fPYsWMH/Pz8sHnzZq32W7dujQ0bNiAgIAD169eHTCZDp06d4O7ujiZNmqBp06Y4cOAAmjZtipYtWyIlJQXbtm2Dr68vtmzZkq9z6dixIyZPnoxp06ahSpUq6NixI+RyOe7du4dLly4hNjYW06dPz3cC88UXXyAtLQ3NmzdHxYoVoVAocP78eezduxdCCCxcuFC9rg79v4rNNB/Lm+snDiIiokJw69YtAEBte913V1TlqnpEKkxg3gJXV1ckJiZi4cKFiIyMxIoVKyCTyeDo6IiWLVsiICBAXdfS0hIHDhzAuHHjcPDgQURHR6NWrVpYs2YNypUrpzOBWbRoEQDgwIED2LJlC7Kzs+Hg4AB3d3dIkoRt27Zh9OjR2LlzJ06fPo26deti27ZtuHnzZr4TGACYOnUqWrZsie+++w779+/HgwcPYGdnB1dXVwQHB+Pjjz/Od5tjxoxBREQEjh07hh07dkCpVMLR0RG9evXC8OHD0bRp03y3WWTlt1uXyst3U64e+e8ODKA5HuZNj0VERPSOOTo6AgDOpCrRxFn7I+mZVKVGPSIVSQgh9B1EURUWFobAwECsXLlSY9V70i9vb2/ExMTgvX/qvjiIf0N/IO084DMv585LymFg1zigbHXgoxcmeuAgfiIiKiaUSiWqVHKBu9kdRPY01ehGli0E/NZn4EyGAy4mXeEYGNLAhSzzIDAwEJIkoVevXvoOpcRSKBSQJAmSJCEmJkbf4bwbFmX++/noN8DBPWfMy4KqOf86uAMfrdKsR0REVEzIZDIs+HYRdlxQwG99BuKuKfD4uUDctZzHOy4oMH9hKJMX0sIuZK9Qr149BAUFqR+X6Cl99czAwEDjd1HiWNgB/SO5DgwREb1X/P39sWnTJowZ9SWarfhvgiJXeQVs2hTKKZRJJ3Yho0KRnJyMsLCw19aztrbGyJEj33o8REREVHwolUrExsbi1q1bcHR0hKenJ++8UK6YwFChiI6ORqtWrV5bTy6XIzk5+e0HRERERETvJSYwRERERERUbHAQPxERERERFRtMYIiIiIiIqNhgAkNERERERMUGExgiIiIiIio2uA4MlRhKpRLZ2dn6DoOIiAgAYGRkpO8Q3jq+91J+5eV1wQSGSoxr164hMzNT32EQEREBAKpWrarvEN46vvdSfuXldcEEhkqMzMxMyGQyGBryaU9ERPqlUCj0HcI7wfdeyo+8vi74bKISxdDQsETcsiciIioq+N5LhY2D+ImIiIiIqNhgAkNERERERMUGExgiIiIiIio2mMAQEREREVGxwQSGiIiIiIiKDc5CRkRERER6pVQqER8fj9TUVNjb28PDwwMymUzfYVERxQSGiN7YlXvPcP1hJpxLG8PVzkzf4RARUTGya9cuBAWH4OaN6+qy8k7OCAkOgo+Pjx4jo6KKCQwRFdiDZwpM3HUFiTf+VZfVd7LEDB9XWJvxzwsREb3arl27MGzYMJhVbgSHviNhVFaOrLQUPDj6f+zdf1zUZb7//8ebQcVEEiEEQwd2UbcVU2+poK2K9rEMIxGppW8/FEtr9+OnzM1zWo8GmLtWR02rc9psS6w1SlGBo6btKiAmkCfpJHvyFwkqoiioefyFM8z3D85MTgMKrjggz/vtNjec67re73nNwDi8eF+v61rFtGnTWLZsmZIYcWHYbDabu4MQuRn27dtHhw4dtJnWP+DCZavT/X/6j+85UHWJP07ox+BQX3YePMXsdbsJ8+vAGzE/c4zr2E7TAERErnT58mXMZrO7w2h2V/vstVqtRA4dxumO3fGPm4Nh/FiabbPVcnLtfLpcrKBgx5eaTtZGNPZ9oSL+q0hNTcUwDMctISHBqb+wsJDRo0fj5+eHYRhERUW5J9A2wGKxOH0vDMNwd0ht0n3vfut0+/rI//DHCf0Yd3cQAZ29GHd3EH+YEM7XR/7HaZyIiMhPFRYWcrT8CD6RjzolLwCG4YFP5CMcPXKYwsJCN0UoLZXmeDTC+PHjGTBgAOHh4Y62M2fOEBMTQ01NDU8++SR+fn6EhITclHgMw2DkyJHk5OTclMe7WcaNG8fGjRvp0KEDFy9edOrz8PAgKSkJqEssy8rK3BGi1GNwqK/T/SGhXd0UiYiItCaVlZUAtLuj/r+4t/M3O40TsVMC0wixsbFMnjzZqW3nzp2cOHGCBQsW8PLLL7snsFvIhx9+yKZNm/Dy8qK+WY0eHh4kJycDkJOTowTGTbb85m7Hv0urL/L0Z/vYefAU4+4OcrR/dbAagA9+3ZuQrl43PUYREWkdAgICALh8oowOd/7Cpf/yyTKncSJ2mkJ2nY4ePQpAYGCgmyNp/Y4cOcLMmTOZMWMG3bp1c3c4chUd25kct7u6dWJwj87MzdzN+m+PUnn2Iuu/PcormcUM6dmZu7p1cowVERH5qYiICLrfGcwPBauw2Wqd+my2Wn4oWE334B5ERES4KUJpqZTAXAfDMJg0aRIAiYmJjpqMK6d0VVZW8uKLLxIWFkaHDh3w9/dn4sSJFBcXu5wvOzubKVOm0KdPH7y9vfH29mbQoEEsW7bMaVxOTo6j9iM3N9epHiQ1NRWA5ORkl1js7DU99rEApaWlGIbB5MmT2bNnD3Fxcfj7+2MYBqWlpY5xmZmZ3Hffffj6+uLl5UV4eDgLFy7EarW6PE5TPf3009xxxx3Mnz//Hz6X3FwpY0MI8/Ni+idFDPnDFqZ/UkSYnxfJD4S4OzQREWnhTCYTKclJXCjZycm187lU/h21l85zqfw7Tq6dz4WSnaQkvaICfnGhKWTXISkpiW+++YbMzExHfQzgqIEpKSkhKiqK8vJy7r//fmJjY6msrGTNmjVs3ryZLVu2OP014fXXX+fAgQNERkYyYcIETp8+zaZNm3j22WfZu3cvixYtcpw/KSmJlJQUzGaz07Q2ewzXy/74ffv2ZdKkSVRXV9O+fXsAZs+ezYIFCwgODmbixIn4+Piwbds2Zs2aRWFhIatXr77ux122bBl//etfyc7OpmNH7R/S2nTp6MmS2DDtAyMiItclOjqaZcuW1e0D85dZjvbuwT1YqiWUpQFKYK5DcnIyqampZGZm1lsf89RTT3Hs2DE2b97MmDFjHO1z5sxh0KBBTJ06lW+//XFlpnfffZfQ0FCnc1gsFqKjo1m6dCkvvPACPXv2JCQkhOTkZFJSUhz/vlG+/PJL5s6dy7x585za//rXv7JgwQIefPBB0tPTue222wCw2Wz89re/5U9/+hNr1qxh4sSJTX7MsrIyXnrpJZ577jlGjhx5Q56HuEeoX0clLiIicl2io6N54IEHKCwspLKykoCAACIiInTlRRqkKWQ3WFFRETt27GDSpElOyQtA7969mTp1Krt373aaSvbT5AXA09OT5557DqvVSnZ2drPHHRgYyJw5c1za33nnHQDee+89R/ICddPoXnvtNQzDIC0trcmPZ7PZePrpp/H19eX111+//sBFRESk1TOZTAwbNozY2FiGDRum5EWuSldgbrCCggIAjh07Vu8Vkj179ji+2pdlPnv2LAsXLiQjI4OSkhLOnTvndIx9wYDm1L9/f8eUsSsVFBTQqVMnPvjgg3qP69ixo+M5NcW7777Lli1b+Pzzz+ncuXOTjxcREZFbh9Vq1RUYaTQlMDdYdXXdErIbNmxgw4YNDY6zJyk1NTVERUWxa9cuBg4c6NhTxtPTk9LSUlasWMGlS5eaPe6GVv+qrq7GYrGQkpLS4LE/Tbiupby8nH/+539m8uTJjB07tknHSsvkeeoApjOHsN7eE4tvmLvDERGRVmTjxo3MS36Fw+UVjrYedwbxSvI81cBIvZTA3GA+Pj4AvP3220yfPv2a4zMzM9m1axfPPPMM77//vlPfp59+yooVK5r0+B4edbMCLRaLS9+ZM2caPK6hne19fHwwDIOTJ082KY6r2b9/P//zP/9Damqq04po9cVz6tQpunTpcsMeW24sj4un8P3rC3So2OlouxQ0mFNjllLr5XuVI0VEROqSl2nTpvFQL09WPX0b4QEmiiut/DHvJNOmTWOZCvmlHkpgbjD76mL5+fmNSmBKSkoAePjhh1368vLy6j3Gw8OjweWLfX3rfmksLy936SsqKrpmPD8VERHB559/zv79++nVq1eTj69PUFAQTz/9dL19n332GRcuXHAsjNChQ4cb8phyYxiXzzvd9/3bi3Q4tR8eSYWew+DQDjr8xwx8/zaT6gf+zeV4W7vbXNpERKRtslqtzEt+hYd6eZKR4IXH//7xMjLYk4wEE7GfXuTVlCQeeOABTScTJ0pgbrAhQ4YQERFBWloaDz/8ML/+9a+d+mtra8nLy3OsumU2mwHYvn07MTExjnG5ubkuV2TsunbtypEjR+rtGzRoEAAfffQRTz75pOOKTH5+PitXrmzy83n++ef5/PPPmTJlChkZGfj5+Tn1Hzt2jFOnTnHXXXc1+px9+vThz3/+c719f/vb3zh27FiD/eJeQcvvcW18JBX6Tqj7d98JYLPRIT2x3rFHp33XvAGKiEirUVhYyOHyClY9fZsjebHzMAx+/6t2DPvwKIWFhQwbNsxNUUpLpASmGaSlpTFq1CgSEhJYsmQJ99xzD15eXhw6dIj8/HxOnDjBxYsXAYiJiSEkJIQ33niD4uJiwsPD2bt3L+vXryc2NpY1a9a4nH/06NGsWrWK+Ph4Bg4ciMlkYty4cfTr14/IyEiGDh3K1q1bGTp0KCNGjKCsrIysrCxiYmJYt25dk57L2LFjmTt3Lq+++iphYWGMHTsWs9lMVVUVBw4cIC8vj/nz5zcpgZFbTM+ffKiY73VPHCIi0qpUVlYCEB5Q/9UVe7t9nIidEphmEBoaSlFREYsXLyYjI4MPP/wQk8lEUFAQI0aMID4+3jHW29ubrVu3MmvWLLZt20ZOTg59+/Zl5cqVdOvWrd4EZunSpQBs3bqVdevWUVtbS2BgIP369cMwDLKyspg5cyYbNmxg9+7d9O/fn6ysLI4ePdrkBAZg3rx5jBgxgrfeeostW7Zw+vRp/Pz8CA0NJTk5mccff/z6XyxpVSoSv3b82/P099yx7hE4tOPHKzAAZV8CcGLCaixdfnazQxQRkVYiICAAgOJKK5HBrr+SFldancaJ2Bk2m83m7iBaqtTUVBITE1m+fLnLZpXiPlFRUeTm5tLUH919+/bRoUMH2rVr10yRtT1+G56m/am9GNEL6668lH2JbeNL1HTtQ1V0/Utvi4gIXL582TGN/FZ2tc9eq9XKvUMjGNDppFMNDECtzUbspxf5r/N3sH1HgWpg2ojGvi+0kWUjJCYmYhgGCQkJ7g6lzbJYLBiGgWEY5Obmujsc+V+n7ltITdc+kJ4Ii3pDeiI1XftwavRCd4cmIiItnMlk4pXkeazfbyH204vkH7Zw9pKN/MN199fvtzA3KUXJi7jQFLKrGDBgAElJSY779o0n5ebz8PBw+l5Iy1Dr5UtV9AfaB0ZERK5LdHQ0y5YtY17yKwz78Md9YHoGd2fZshQtoSz10hQyuSFKS0sb3NPlSl26dGHGjBnNHk99NIVMRERaCk0hc2a1WiksLKSyspKAgAAiIiJ05aUNauz7Qldg5IYoLS0lJSXlmuPMZrPbEhgRERFpmUwmk5ZKlkZTAiM3RFRUVJOL6kVEREREmkpF/CIiIiIi0moogRERERERkVZDCYyIiIiIiLQaSmBERERERKTVUBG/tCkWi8XdIYiIiLSpz6O29FzlH9PYnxUlMNJmtG/fnpqaGqxWq7tDERERaRP02SvNQRtZSpthtVqpra11dxgiIiIAbWJjZX32SlM15n2hBEZERERERFoNFfGLiIiIiEiroQRGRERERERaDSUwIiIiIiLSaiiBERERERGRVkMJjIiIiIiItBpKYEREREREpNXQRpYiIiIi4lZWq5W8vDwqKioICgpi+PDhmEwmd4clLZQSGJFb0P7jZymtOk+I32306tbZ3eGIiIg0aO3atcx4cSaHD5U52nr0NLPkzcXExcW5MTJpqbSRpcgtpPpcDc+nFbH9wElH26/C/HnrsYF07dTejZGJiIi4Wrt2LfHx8XT8+WB8Ih+l3R1mLp8o44eCVVwo2Ul6erqSGHGhBEbkFvLEnwv5ruIH5o0PZ3CoLzsPnuKVzGJ+2d2Hj5+OcHd4IiIiDlarldCf/Zyq9t3wj5uDYfxYmm2z1XJy7Xz8LldysOSAppOJExXxX0VqaiqGYThuCQkJTv2FhYWMHj0aPz8/DMMgKirKPYG2ARaLxel7YRiGu0NqcfYfP8v2AyeZNz6ccXcHEdDZi3F3B5Eyvi95+0+y//hZd4coIiLikJeXx+FDZfhEPuqUvAAYhgc+kY9wuKyUvLw8N0UoLZVqYBph/PjxDBgwgPDwcEfbmTNniImJoaamhieffBI/Pz9CQkJuSjyGYTBy5EhycnJuyuM1h9LSUkJDQxvsT0tLc0oYPTw8SEpKAuoSy7KysoYObbNKq84DMDjU16l9SGhXR7/qYUREpKWoqKgAoN0d5nr72/mbncaJ2CmBaYTY2FgmT57s1LZz505OnDjBggULePnll90T2C2gf//+xMbGurRfmSxCXQKTnJwMQE5OjhKYeoT43QbAzoOnGHd3kKP9q4PVTv0iIiItQVBQ3WfV5RNldLjzFy79l0+WOY0TsVMCc52OHj0KQGBgoJsjad0GDBjgSEzkH9OrW2d+FebPK5nF2LAxJLQrXx2sJinz7wzv5a+rLyIi0qIMHz6cHj3NVBWsqrcG5oeC1fQwhzB8+HA3RiktkWpgroNhGEyaNAmAxMRER03GlVO6KisrefHFFwkLC6NDhw74+/szceJEiouLXc6XnZ3NlClT6NOnD97e3nh7ezNo0CCWLVvmNC4nJ8dR+5Gbm+tUD5KamgpAcnKySyx29poe+1iom8plGAaTJ09mz549xMXF4e/vj2EYlJaWOsZlZmZy33334evri5eXF+Hh4SxcuBCr1Xp9L6I0i7ceG8gvu/sw/ZMihvxhC9M/KeKX3X1YmjDQ3aGJiIg4MZlMLHlzMRdKdnJy7XwulX9H7aXzXCr/jpNr53OhZCdLFi9SAb+40BWY65CUlMQ333xDZmamoz4GcNTAlJSUEBUVRXl5Offffz+xsbFUVlayZs0aNm/ezJYtW4iI+HFFqNdff50DBw4QGRnJhAkTOH36NJs2beLZZ59l7969LFq0yHH+pKQkUlJSMJvNTtPa7DFcL/vj9+3bl0mTJlFdXU379nXL7s6ePZsFCxYQHBzMxIkT8fHxYdu2bcyaNYvCwkJWr1593Y979OhR3n33XU6fPk337t257777CA4O/oeeS1vWtVN7Pn46QvvAiIhIqxAXF0d6enrdPjB/meVo72EOYYmWUJaG2KRBy5cvtwG25cuXN6lv2LBhNk9PT9sXX3zh1L53715b586dbf369XNq//77713OcfnyZduYMWNsJpPJVlZW5tQH2EaOHFlvzElJSTbAlp2d3aiYDx48aANsgG3u3Lkux3zxxRc2wPbggw/azp0752ivra21PffcczbAlp6eXm8sV3Pl41558/T0tM2cOdNmtVobPHbkyJE2/eiKiIjcOiwWiy07O9v2ySef2LKzs20Wi8XdIUkLpilkN1hRURE7duxg0qRJjBkzxqmvd+/eTJ06ld27dztNJatvNS5PT0+ee+45rFYr2dnZzR53YGAgc+bMcWl/5513AHjvvfe47bYfi8ANw+C1117DMAzS0tKa/Hi33Xab40rWDz/8QGVlJVlZWfTq1YvFixfzL//yL9f/ZERERKRVMZlMREVF8dhjjxEVFaVpY3JVmkJ2gxUUFABw7NixeovT9+zZ4/hqX2nr7NmzLFy4kIyMDEpKSjh37pzTMfYFA5pT//79HVPGrlRQUECnTp344IMP6j2uY8eOjufUFAEBAU6vT+fOnYmJiWHw4MGEh4ezePFi/umf/glfX9+GTyIiIiK3BKvVSl5eHhUVFQQFBTF8+HAlMdIgJTA3WHV13ZK1GzZsYMOGDQ2OsycpNTU1REVFsWvXLgYOHOjYU8bT05PS0lJWrFjBpUuXmj3ubt261dteXV2NxWIhJSWlwWN/mnD9IwIDA4mOjubjjz9m586d3H///Tfs3G1K5R6o/h66/gwCXJemFBERaSnWrl3L7158gdJDRxxtIT2DWfTmUtXASL2UwNxgPj4+ALz99ttMnz79muMzMzPZtWsXzzzzDO+//75T36effsqKFSua9PgeHnWzAi0Wi0vfmTNnGjyuoZ3tfXx8MAyDkydPNimOf4S/vz8A58+fv2mPecs4sQ/Wz4CyL39sM98Lj34MnfzcFpaIiEh91q5dS3x8PA/18iTt6dsIDzBRXGnlj9uPEx8fT7oK+aUeqoG5weyri+Xn5zdqfElJCQAPP/ywS19eXl69x3h4eDS4fLF9ylV5eblLX1FRUaNiulJERARVVVXs37+/ycder6+++gr4cVU3aUDNOdfbv0fA8b/DI6nwu311X4//HdInu44VERFxI6vVyu9efIGHenmSkeBFZLAn3u0NIoM9yfi1Fw/19uSlmTO0ZYO4UAJzgw0ZMoSIiAjS0tL47LPPXPpra2vJzc113DebzQBs377daVxubq7LFRm7rl27cuTIkXr7Bg0aBMBHH31EbW2toz0/P5+VK1c27ckAzz//PABTpkyhqqrKpf/YsWN89913TT7vV199xeXLl13aFy9ezJdffskvf/lL+vfv3+Tztil/7O56s9VCzBLoOwE6d6v7+tCbcHCb61gRERE3ysvLo/TQEWYPb4fHT2aCeBgGv7+3HQfLDjf4B11puzSFrBmkpaUxatQoEhISWLJkCffccw9eXl4cOnSI/Px8Tpw4wcWLFwGIiYkhJCSEN954g+LiYsLDw9m7dy/r168nNjaWNWvWuJx/9OjRrFq1ivj4eAYOHIjJZGLcuHH069ePyMhIhg4dytatWxk6dCgjRoygrKyMrKwsYmJiWLduXZOey9ixY5k7dy6vvvoqYWFhjB07FrPZTFVVFQcOHCAvL4/58+dz1113Nem8//RP/8SePXsYOXIkPXr04MKFC+Tn51NUVISvry8ff/xxg9Pa5Bp6DnO+b77XPXGIiIhcRUVFBQDhAfUX69vb7eNE7JTANIPQ0FCKiopYvHgxGRkZfPjhh5hMJoKCghgxYgTx8fGOsd7e3mzdupVZs2axbds2cnJy6Nu3LytXrqRbt271JjBLly4FYOvWraxbt47a2loCAwPp168fhmGQlZXFzJkz2bBhA7t376Z///5kZWVx9OjRJicwAPPmzWPEiBG89dZbbNmyhdOnT+Pn50doaCjJyck8/vjjTT7nE088wZo1a9ixY4ejvsZsNvPCCy/w0ksvaTPLxpj9k9XpTuyD96Pg0I66Ky929nqYqTlwR++bFZ2IiMhVBQUFAVBcaSUy2PVX0uJKq9M4ETvDZrPZ3B1ES5WamkpiYiLLly932vVe3CsqKorc3Fz0o1uP5dFwYi9E/2vdlZeyL2HjLAjsB09luDs6ERERB6vVStjPQujX8TgZv/ZymkZWa7MR+9lFii8Gsr/koJZUFieqgWmExMREDMMgISHB3aG0WRaLBcMwMAzDqYZIfuLRj+uSlfREWNS77mtgP5hY/z4+IiIi7mIymVj05lLW77MQ+9lF8g9bOHvJRv7huvvr91lYuHiJkhdxoSlkVzFgwACSkpIc9+0bT8rN5+Hh4fS9kAZ08qu70qJ9YEREpBWIi4sjPT2d3734AsM+/HGBolBzD9LTl2gJZamXppDJDVFaWkpqauo1x3Xp0oUZM2Y0ezwiIiLSelitVvLy8qioqCAoKIjhw4fryos0SAmM3BA5OTmMGjXqmuPMZjOlpaXNH5CIiIiI3JKUwIiIiIiISKuhIn4REREREWk1lMCIiIiIiEiroQRGRERERERaDSUwIiIiIiLSamgfGGkzrFYrtbW17g5DREQEgHbt2rk7hGanz15pqsa8L5TASJtx+PBhampq3B2GiIgIAL1793Z3CM1On73SVI15XyiBkTajpqYGk8mEp6d+7EVExL0sFou7Q7gp9NkrTdHY94V+mqRN8fT0bBOX7EVERFoKffbKjaYifhERERERaTWUwIiIiIiISKuhBEZERERERFoNJTAiIiIiItJqKIEREREREZFWQ6uQiYiIiIhbWa1WCgsLqaysJCAggIiICEwmk7vDkhZKCYzILehg1QWOnKkh+Pb2hPp1dHc4IiIiDdq4cSNJySkcLT/iaOt+ZzApyUlER0e7MTJpqZTAiNxCTl+wkLSplJ2HzzraBvfoTMrYELp01NtdRERalo0bNzJt2jQ6/nwwgU/MoN0dZi6fKON0wSqmTZvGsmXLlMSIC8Nms9ncHYTIzbBv3z46dOhwS2+m9cK6A5RUX+TV8f0YHOrLzoOnmJu5mzA/L5bEhrk7PBER+V+XL1/GbDa7O4xmd7XPXqvVSuTQYZzu2B3/uDkYxo+l2TZbLSfXzqfLxQoKdnyp6WRtRGPfFyriv4rU1FQMw3DcEhISnPoLCwsZPXo0fn5+GIZBVFSUewJtAywWi9P3wjAMd4fU4hysusDOw2d5dXw/xt0dREBnL8bdHcS88eF8degsB6suuDtEERERh8LCQo6WH8En8lGn5AXAMDzwiXyEo0cOU1hY6KYIpaXSnJJGGD9+PAMGDCA8PNzRdubMGWJiYqipqeHJJ5/Ez8+PkJCQmxKPYRiMHDmSnJycm/J4zammpoZ33nmHTz75hL179wJgNpsZOXIk//Zv/+YY5+HhQVJSElCXWJaVlbkl3pbsyJkaAAaH+jq1Dwnt6uhXPYyIiLQUlZWVALS7o/6/uLfzNzuNE7FTAtMIsbGxTJ482alt586dnDhxggULFvDyyy+7J7BW7tSpU4wdO5avvvqKYcOG8eyzzwJw8OBBPvvsM5cEJjk5GYCcnBwlMPUIvr09ADsPnmLc3UGO9q8OVjv1i4iItAQBAQEAXD5RRoc7f+HSf/lkmdM4ETslMNfp6NGjAAQGBro5ktbr6aefZufOnaxcuZL/7//7/5z6LBaLm6JqvUL9OjK4R2fmZu7Gho0hoV356mA1r2QWM6RnZ119ERGRFiUiIoLudwZzumBVvTUwPxSspntwDyIiItwYpbREqoG5DoZhMGnSJAASExMdNRlXTumqrKzkxRdfJCwsjA4dOuDv78/EiRMpLi52OV92djZTpkyhT58+eHt74+3tzaBBg1i2bJnTuJycHEftR25urlM9SGpqKgDJyckusdjZa3rsYwFKS0sxDIPJkyezZ88e4uLi8Pf3xzAMSktLHeMyMzO577778PX1xcvLi/DwcBYuXIjVar2u17CwsJB169bxxBNPuCQvAJ6eyq2vR8rYEML8vJj+SRFD/rCF6Z8UEebnRfIDIe4OTURExInJZCIlOYkLJTs5uXY+l8q/o/bSeS6Vf8fJtfO5ULKTlKRXVMAvLvRb4nVISkrim2++ITMz01EfAzhqYEpKSoiKiqK8vJz777+f2NhYKisrWbNmDZs3b2bLli1Of014/fXXOXDgAJGRkUyYMIHTp0+zadMmnn32Wfbu3cuiRYsc509KSiIlJQWz2ew0rc0ew/WyP37fvn2ZNGkS1dXVtG9fN+Vo9uzZLFiwgODgYCZOnIiPjw/btm1j1qxZFBYWsnr16iY/3meffQbAI488wsmTJ8nKyuL48eP06NGDBx98ED8/v3/o+bRVXTp6siQ2TPvAiIhIqxAdHc2yZcvq9oH5yyxHe/fgHizVEsrSACUw1yE5OZnU1FQyMzPrrY956qmnOHbsGJs3b2bMmDGO9jlz5jBo0CCmTp3Kt99+62h/9913CQ0NdTqHxWIhOjqapUuX8sILL9CzZ09CQkJITk4mJSXF8e8b5csvv2Tu3LnMmzfPqf2vf/0rCxYs4MEHHyQ9PZ3bbrsNAJvNxm9/+1v+9Kc/sWbNGiZOnNikx/vP//xPoC5xevLJJzlz5oyjz9vbmz//+c/8+te//gefVdsV6tdRiYuIiLQK0dHRPPDAAxQWFlJZWUlAQAARERG68iIN0hSyG6yoqIgdO3YwadIkp+QFoHfv3kydOpXdu3c7TSX7afICdVOonnvuOaxWK9nZ2c0ed2BgIHPmzHFpf+eddwB47733HMkL1E2je+211zAMg7S0tCY/nn1FkVmzZjF+/HhKSko4deoUf/nLX/Dw8ODJJ590SvJERETk1mUymRg2bBixsbEMGzZMyYtcla7A3GAFBQUAHDt2rN4rJHv27HF8tS/LfPbsWRYuXEhGRgYlJSWcO3fO6Rj7ggHNqX///o4pY1cqKCigU6dOfPDBB/Ue17FjR8dzaora2loA7r77bkdtDsDjjz/O2bNn+c1vfsNbb73Fn//85yafW0RERFoXq9WqKzDSaEpgbrDq6rolazds2MCGDRsaHGdPUmpqaoiKimLXrl0MHDjQsaeMp6cnpaWlrFixgkuXLjV73N26dau3vbq6GovFQkpKSoPH/jThaozbb78dgIceeshlU8qYmBh+85vfOKaZSdN5njqA6cwhrLf3xOIb5u5wREREGrRx40bmJb/C4fIKR1uPO4N4JXmeamCkXkpgbjAfHx8A3n77baZPn37N8ZmZmezatYtnnnmG999/36nv008/ZcWKFU16fA+PulmB9S1DfGWdyU81tLO9j48PhmFw8uTJJsVxLX369OE///M/6dKli0ufve3CBe0c31QeF0/h+9cX6FCx09F2KWgwp8YspdbL9ypHioiI3HwbN25k2rRpPNTLk1VP30Z4gIniSit/zDvJtGnTWKZCfqmHamBuMPvqYvn5+Y0aX1JSAsDDDz/s0peXl1fvMR4eHg0uX+zrW/dLanl5uUtfUVFRo2K6UkREBFVVVezfv7/Jx17N6NGjAfjv//5vlz57m31VN2mYcfm80833by/S4dR+eCQVfrcPHkmlw6n9+P5tpstYERERd7JarcxLfoWHenmSkeBFZLAn3u0NIoPr7j/Uy5NXU5Kue8sGuXUpgbnBhgwZQkREBGlpaY6lgq9UW1tLbm6u477ZbAZg+/btTuNyc3NdrsjYde3alSNHjtTbN2jQIAA++ugjR50J1CVUK1eubNqTAZ5//nkApkyZQlVVlUv/sWPH+O6775p83vj4ePz9/Vm5ciW7d+92tNfU1JCUlATAo48+2uTztjVBy+9xunU4WggxS6DvBOjcre7rQ2/S4WiBy1gRERF3Kiws5HB5BbOHt8PjJzNBPAyD3/+qHYeOHKWwsNBNEUpLpSlkzSAtLY1Ro0aRkJDAkiVLuOeee/Dy8uLQoUPk5+dz4sQJLl68CNTVe4SEhPDGG29QXFxMeHg4e/fuZf369cTGxrJmzRqX848ePZpVq1YRHx/PwIEDMZlMjBs3jn79+hEZGcnQoUPZunUrQ4cOZcSIEZSVlZGVlUVMTAzr1q1r0nMZO3Ysc+fO5dVXXyUsLIyxY8diNpupqqriwIED5OXlMX/+fO66664mndfHx4f333+f+Ph4IiMjiY+Px9fXl7/97W/8/e9/Jzo62mV5ammknsOc75vvdU8cIiIiV2FfkTQ8oP5ifXu7fZyInRKYZhAaGkpRURGLFy8mIyODDz/8EJPJRFBQECNGjCA+Pt4x1tvbm61btzJr1iy2bdtGTk4Offv2ZeXKlXTr1q3eBGbp0qUAbN26lXXr1lFbW0tgYCD9+vXDMAyysrKYOXMmGzZsYPfu3fTv35+srCyOHj3a5AQGYN68eYwYMYK33nqLLVu2cPr0afz8/AgNDSU5OZnHH3/8ul6n2NhYcnNzmT9/PllZWZw/f55evXrx+uuvM3PmTK0+0ggViV87/u15+nvuWPcIHNpRd+XFruxLAE5MWI2ly89udogiIiL1CggIAKC40kpksOuvpMWVVqdxInaGzWazuTuIlio1NZXExESWL1+uqwEtSFRUFLm5uTT1R3ffvn106NCBdu3aNVNk7ue34Wnan9qLEb2w7spL2ZfYNr5ETdc+VEXXvxS2iIjcfJcvX3ZMI7+VXe2z12q1cu/QCAZ0OklGgpfTNLJam43YTy/yX+fvYPuOAv1Rs41o7PtCNTCNkJiYiGEYJCQkuDuUNstisWAYBoZhONUQibNT9y2kpmsfSE+ERb0hPZGarn04NXqhu0MTERFxYjKZeCV5Huv3W4j99CL5hy2cvWQj/3Dd/fX7LcxNSlHyIi40hewqBgwY4CgoBxwbT8rN5+Hh4fS9kPrVevlSFf2B9oEREZFWITo6mmXLljEv+RWGffjjPjA9g7uzbFmKllCWemkKmdwQpaWlpKamXnNcly5dmDFjRrPHU5+2MIVMRERaB00hc2a1WiksLKSyspKAgAAiIiJ05aUNauz7Qldg5IYoLS0lJSXlmuPMZrPbEhgRERFpmUwmE8OGDbv2QBGUwMgNEhUV1eSiehERERGRplIRv4iIiIiItBpKYEREREREpNVQAiMiIiIiIq2GEhgREREREWk1VMQvbYrFYnF3CCIiIm3q86gtPVf5xzT2Z0UJjLQZ7du3p6amBqvV6u5QRERE2gR99kpz0EaW0mZYrVZqa2vdHYaIiAhAm9hYWZ+90lSNeV8ogRERERERkVZDRfwiIiIiItJqKIEREREREZFWQwmMiIiIiIi0GkpgRERERESk1VACIyIiIiIirYb2gRERERERt7JareTl5VFRUUFQUBDDhw/HZDK5OyxpoZTAiNyC9h8/S2nVeUL8bqNXt87uDkdERKRBa9euZcaLMzl8qMzR1qOnmSVvLiYuLs6NkUlLpX1gRG4h1edqeD6tiO0HTjrafhXmz1uPDaRrp/ZujExERMTV2rVriY+Pp+PPB+MT+Sjt7jBz+UQZPxSs4kLJTtLT05XEiAslMCK3kCf+XMh3FT8wb3w4g0N92XnwFK9kFvPL7j58/HSEu8MTERFxsFqthP7s51S174Z/3BwM48fSbJutlpNr5+N3uZKDJQc0nUycqIj/KlJTUzEMw3FLSEhw6i8sLGT06NH4+flhGAZRUVHuCbQNsFgsTt8LwzDcHVKLs//4WbYfOMm88eGMuzuIgM5ejLs7iJTxfcnbf5L9x8+6O0QRERGHvLw8Dh8qwyfyUafkBcAwPPCJfITDZaXk5eW5KUJpqVQD0wjjx49nwIABhIeHO9rOnDlDTEwMNTU1PPnkk/j5+RESEnJT4jEMg5EjR5KTk3NTHq85TJ48mRUrVlx1zLx585g7dy4AHh4eJCUlAXWJZVlZ2dUObZNKq84DMDjU16l9SGhXR7/qYUREpKWoqKgAoN0d5nr72/mbncaJ2CmBaYTY2FgmT57s1LZz505OnDjBggULePnll90TWCsWGxvbYMK3cOFCzp07xwMPPOBo8/DwIDk5GYCcnBwlMPUI8bsNgJ0HTzHu7iBH+1cHq536RUREWoKgoLrPqssnyuhw5y9c+i+fLHMaJ2KnBOY6HT16FIDAwEA3R9I6xcbGEhsb69L+9ddfk5KSQr9+/RgyZMjND6wV69WtM78K8+eVzGJs2BgS2pWvDlaTlPl3hvfy19UXERFpUYYPH06PnmaqClbVWwPzQ8FqephDGD58uBujlJZINTDXwTAMJk2aBEBiYqKjJuPKKV2VlZW8+OKLhIWF0aFDB/z9/Zk4cSLFxcUu58vOzmbKlCn06dMHb29vvL29GTRoEMuWLXMal5OT46j9yM3NdaoHSU1NBSA5OdklFjt7TY99LEBpaSmGYTB58mT27NlDXFwc/v7+GIZBaWmpY1xmZib33Xcfvr6+eHl5ER4ezsKFC7Fardf3Ijbgz3/+MwBPP/30DT1vW/HWYwP5ZXcfpn9SxJA/bGH6J0X8srsPSxMGujs0ERERJyaTiSVvLuZCyU5Orp3PpfLvqL10nkvl33Fy7XwulOxkyeJFKuAXF7oCcx2SkpL45ptvyMzMdNTHAI4pUSUlJURFRVFeXs79999PbGwslZWVrFmzhs2bN7NlyxYiIn5cEer111/nwIEDREZGMmHCBE6fPs2mTZt49tln2bt3L4sWLXKcPykpiZSUFMxms9O0NnsM18v++H379mXSpElUV1fTvn3dsruzZ89mwYIFBAcHM3HiRHx8fNi2bRuzZs2isLCQ1atX/0OPbXfhwgXS0tLo0KEDTz755A05Z1vTtVN7Pn46QvvAiIhIqxAXF0d6enrdPjB/meVo72EOYYmWUJaG2KRBy5cvtwG25cuXN6lv2LBhNk9PT9sXX3zh1L53715b586dbf369XNq//77713OcfnyZduYMWNsJpPJVlZW5tQH2EaOHFlvzElJSTbAlp2d3aiYDx48aANsgG3u3Lkux3zxxRc2wPbggw/azp0752ivra21PffcczbAlp6eXm8sTfXRRx/ZAFtCQsJVx40cOdKmH10REZFbh8VisWVnZ9s++eQTW3Z2ts1isbg7JGnBNIXsBisqKmLHjh1MmjSJMWPGOPX17t2bqVOnsnv3bqepZKGhoS7n8fT05LnnnsNqtZKdnd3scQcGBjJnzhyX9nfeeQeA9957j9tu+7EI3DAMXnvtNQzDIC0t7YbE8MEHHwDwzDPP3JDziYiISOtgMpmIioriscceIyoqStPG5Ko0hewGKygoAODYsWOOVbOutGfPHsdX+7LMZ8+eZeHChWRkZFBSUsK5c+ecjrEvGNCc+vfv75gydqWCggI6derkSC5+qmPHjo7n9I84cOAA27ZtIzQ0lNGjR//D5xMREZHWw2q1kpeXR0VFBUFBQQwfPlxJjDRICcwNVl1dt2Tthg0b2LBhQ4Pj7ElKTU0NUVFR7Nq1i4EDBzr2lPH09KS0tJQVK1Zw6dKlZo+7W7du9bZXV1djsVhISUlp8NifJlzX44MPPsBmszFlyhRtUnkjVO6B6u+h688gwHVpShERkZZi7dq1/O7FFyg9dMTRFtIzmEVvLlUNjNRLCcwN5uPjA8Dbb7/N9OnTrzk+MzOTXbt28cwzz/D+++879X366afX3Ozxpzw86mYFWiwWl74zZ840eFxDSYOPjw+GYXDy5MkmxdEUVquVFStWYDKZSExMbLbHaRNO7IP1M6Dsyx/bzPfCox9DJz+3hSUiIlKftWvXEh8fz0O9PEl7+jbCA0wUV1r54/bjxMfHk65CfqmHamBuMPvqYvn5+Y0aX1JSAsDDDz/s0peXl1fvMR4eHg0uX+zrW7cLe3l5uUtfUVFRo2K6UkREBFVVVezfv7/JxzbWxo0bqaioYOzYsdx5553N9ji3nJpzrrd/j4Djf4dHUuF3++q+Hv87pE+uf7yIiIibWK1WfvfiCzzUy5OMBC8igz3xbm8QGexJxq+9eKi3Jy/NnHHDt2yQ1k8JzA02ZMgQIiIiSEtL47PPPnPpr62tJTc313HfbDYDsH37dqdxubm5Lldk7Lp27cqRI0fq7Rs0aBAAH330EbW1tY72/Px8Vq5c2bQnAzz//PMATJkyhaqqKpf+Y8eO8d133zX5vFey19do75cm+mN315utFmKWQN8J0Llb3deH3oSD2+ofLyIi4iZ5eXmUHjrC7OHt8PjJTBAPw+D397bjYNnhBv+gK22XppA1g7S0NEaNGkVCQgJLlizhnnvuwcvLi0OHDpGfn8+JEye4ePEiADExMYSEhPDGG29QXFxMeHg4e/fuZf369cTGxrJmzRqX848ePZpVq1YRHx/PwIEDMZlMjBs3jn79+hEZGcnQoUPZunUrQ4cOZcSIEZSVlZGVlUVMTAzr1q1r0nMZO3Ysc+fO5dVXXyUsLIyxY8diNpupqqriwIED5OXlMX/+fO66667req2OHz/Ohg0b6NatGzExMdd1DvmJnsOc75vvdU8cIiIiV1FRUQFAeED9xfr2dvs4ETslMM0gNDSUoqIiFi9eTEZGBh9++CEmk4mgoCBGjBhBfHy8Y6y3tzdbt25l1qxZbNu2jZycHPr27cvKlSvp1q1bvQnM0qVLAdi6dSvr1q2jtraWwMBA+vXrh2EYZGVlMXPmTDZs2MDu3bvp378/WVlZHD16tMkJDMC8efMYMWIEb731Flu2bOH06dP4+fkRGhpKcnIyjz/++HW/VitWrMBisTBp0iQ8PfXj2CSzf7I63Yl98H4UHNpRd+XFzl4PMzUH7uh9s6ITERG5qqCgIACKK61EBrv+DlBcaXUaJ2Jn2Gw2m7uDaKlSU1NJTExk+fLlTrvei3tFRUWRm5uLfnTrsTwaTuyF6H+tu/JS9iVsnAWB/eCpDHdHJyIi4mC1Wgn7WQj9Oh4n49deTtPIam02Yj+7SPHFQPaXHNSSyuJENTCNkJiYiGEYJCQkuDuUNstisWAYBoZhONUQyU88+nFdspKeCIt6130N7AcT69/HR0RExF1MJhOL3lzK+n0WYj+7SP5hC2cv2cg/XHd//T4LCxcvUfIiLjRn5yoGDBhAUlKS475940m5+Tw8PJy+F9KATn51V1q0D4yIiLQCcXFxpKen87sXX2DYhz8uUBRq7kF6+hItoSz10hQyuSFKS0tJTU295rguXbowY8aMZo9HREREWg+r1UpeXh4VFRUEBQUxfPhwXXmRBimBkRsiJyeHUaNGXXOc2WymtLS0+QMSERERkVuSEhgREREREWk1VMQvIiIiIiKthhIYERERERFpNZTAiIiIiIhIq6EERkREREREWg0lMCIiIiIi0mpoI0tpM6xWK7W1te4OQ0REBIB27dq5O4Rmp89eaarGvC+UwEibcfjwYWpqatwdhoiICAC9e/d2dwjNTp+90lSNeV8ogZE2o6amBpPJhKenfuxFRMS9LBaLu0O4KfTZK03R2PeFfpqkTfH09GwTl+xFRERaCn32yo2mIn4REREREWk1lMCIiIiIiEiroQRGRERERERaDSUwIiIiIiLSaiiBERERERGRVkOrkImIiIiIW1mtVgoLC6msrCQgIICIiAhMJpO7w5IWSgmMyC3oYNUFjpypIfj29oT6dXR3OCIiIg3auHEjSckpHC0/4mjrfmcwKclJREdHuzEyaamUwIjcQk5fsJC0qZSdh8862gb36EzK2BC6dNTbXUREWpaNGzcybdo0Ov58MIFPzKDdHWYunyjjdMEqpk2bxrJly5TEiAvDZrPZ3B2EyM2wb98+OnTocEtvpvXCugOUVF/k1fH9GBzqy86Dp5ibuZswPy+WxIa5OzwREflfly9fxmw2uzuMZne1z16r1Urk0GGc7tgd/7g5GMaPpdk2Wy0n186ny8UKCnZ8qelkbURj3xcq4r+K1NRUDMNw3BISEpz6CwsLGT16NH5+fhiGQVRUlHsCbQMsFovT98IwDHeH1OIcrLrAzsNneXV8P8bdHURAZy/G3R3EvPHhfHXoLAerLrg7RBEREYfCwkKOlh/BJ/JRp+QFwDA88Il8hKNHDlNYWOimCKWl0pySRhg/fjwDBgwgPDzc0XbmzBliYmKoqanhySefxM/Pj5CQkJsSj2EYjBw5kpycnJvyeM3lwoULvPvuu/zlL3/h4MGD2Gw2zGYzjz76KNOnT+f22293jPXw8CApKQmoSyzLysrcFXaLdeRMDQCDQ32d2oeEdnX0qx5GRERaisrKSgDa3VH/X9zb+ZudxonYKYFphNjYWCZPnuzUtnPnTk6cOMGCBQt4+eWX3RNYK3b58mVGjRpFYWEhAwYMYNKkSRiGQXZ2NnPmzCEtLY2vvvqK2267DahLYJKTkwHIyclRAlOP4NvbA7Dz4CnG3R3kaP/qYLVTv4iISEsQEBAAwOUTZXS48xcu/ZdPljmNE7FTAnOdjh49CkBgYKCbI2md1q1bR2FhIXFxcaxZs8apb8KECWRkZJCens5TTz3lpghbn1C/jgzu0Zm5mbuxYWNIaFe+OljNK5nFDOnZWVdfRESkRYmIiKD7ncGcLlhVbw3MDwWr6R7cg4iICDdGKS2RamCug2EYTJo0CYDExERHTcaVU7oqKyt58cUXCQsLo0OHDvj7+zNx4kSKi4tdzpednc2UKVPo06cP3t7eeHt7M2jQIJYtW+Y0Licnx1H7kZub61QPkpqaCkBycrJLLHb2mh77WIDS0lIMw2Dy5Mns2bOHuLg4/P39MQyD0tJSx7jMzEzuu+8+fH198fLyIjw8nIULF2K1Wq/rNfz+++8BePDBB1367KuN6JJx06WMDSHMz4vpnxQx5A9bmP5JEWF+XiQ/EOLu0ERERJyYTCZSkpO4ULKTk2vnc6n8O2ovnedS+XecXDufCyU7SUl6RQX84kJXYK5DUlIS33zzDZmZmY76GMBRA1NSUkJUVBTl5eXcf//9xMbGUllZyZo1a9i8eTNbtmxx+mvC66+/zoEDB4iMjGTChAmcPn2aTZs28eyzz7J3714WLVrkOH9SUhIpKSmYzWanaW32GK6X/fH79u3LpEmTqK6upn37uilHs2fPZsGCBQQHBzNx4kR8fHzYtm0bs2bNorCwkNWrVzf58fr27QvApk2beOaZZ5z6Pv/8cy2KcJ26dPRkSWyY9oEREZFWITo6mmXLltXtA/OXWY727sE9WKollKUBSmCuQ3JyMqmpqWRmZtZbH/PUU09x7NgxNm/ezJgxYxztc+bMYdCgQUydOpVvv/3W0f7uu+8SGhrqdA6LxUJ0dDRLly7lhRdeoGfPnoSEhJCcnExKSorj3zfKl19+ydy5c5k3b55T+1//+lcWLFjAgw8+SHp6uqMmxWaz8dvf/pY//elPrFmzhokTJzbp8R566CFiYmJYs2YN99xzDyNHjgTqrjIdOHCAf//3f2fQoEE35sm1QaF+HZW4iIhIqxAdHc0DDzxAYWEhlZWVBAQEEBERoSsv0iBNIbvBioqK2LFjB5MmTXJKXgB69+7N1KlT2b17t9NUsp8mLwCenp4899xzWK1WsrOzmz3uwMBA5syZ49L+zjvvAPDee+85kheom0b32muvYRgGaWlpTX48wzBYt24dL730EkVFRbz55pu8+eabFBUVERsby9ixY6//yYiIiEirYjKZGDZsGLGxsQwbNkzJi1yVrsDcYAUFBQAcO3as3iske/bscXy1L8t89uxZFi5cSEZGBiUlJZw7d87pGPuCAc2pf//+jiljVyooKKBTp0588MEH9R7XsWNHx3NqigsXLpCQkEBhYSGffPIJY8aMwTAMtmzZwvPPP8/nn39OQUEBP//5z5t8bhEREWldrFarrsBIoymBucGqq+uWrN2wYQMbNmxocJw9SampqSEqKopdu3YxcOBAx54ynp6elJaWsmLFCi5dutTscXfr1q3e9urqaiwWCykpKQ0e+9OEqzEWLFhAVlYWmZmZPPzww472Rx55hM6dO/Pggw8yb948VqxY0eRzSx3PUwcwnTmE9faeWHzD3B2OiIhIvTZu3Mi85Fc4XF7haOtxZxCvJM9TDYzUSwnMDebj4wPA22+/zfTp0685PjMzk127dvHMM8/w/vvvO/V9+umnTf4F3sOjblagxWJx6Ttz5kyDxzW0s72Pjw+GYXDy5MkmxXEt9uRu1KhRLn2jRo3CMAy+/vrrG/qYbYHHhWqMi6fpsj2ZDhU7He2XggZzasxSar18r3K0iIjIzbVx40amTZvGQ708WfX0bYQHmCiutPLHvJNMmzaNZSrkl3qoBuYGs68ulp+f36jxJSUlAE5XIezy8vLqPcbDw6PB5Yt9fet+QS0vL3fpKyoqalRMV4qIiKCqqor9+/c3+dirqamp2zX+xIkTLn0nT57EZrPRoUOHG/qYtxLj8vl6b4Ef30u39Bg6nNoPj6TC7/bBI6l0OLUf37/NrPcYERERd7BarcxLfoWHenmSkeBFZLAn3u0NIoPr7j/Uy5NXU5Kue8sGuXUpgbnBhgwZQkREBGlpaXz22Wcu/bW1teTm5jrum81mALZv3+40Ljc31+WKjF3Xrl05cuRIvX32lbs++ugjamtrHe35+fmsXLmyaU8GeP755wGYMmUKVVVVLv3Hjh3ju+++a/J57733XgBSUlKc/mOqra3llVdeAeq/OiN1gpbfU+8NAFstxCyBvhOgc7e6rw+9SYejBQ0fIyIicpMVFhZyuLyC2cPb4fGTmSAehsHvf9WOQ0eOUlhY6KYIpaXSFLJmkJaWxqhRo0hISGDJkiXcc889eHl5cejQIfLz8zlx4gQXL14EICYmhpCQEN544w2Ki4sJDw9n7969rF+/ntjYWJdd6gFGjx7NqlWriI+PZ+DAgZhMJsaNG0e/fv2IjIxk6NChbN26laFDhzJixAjKysrIysoiJiaGdevWNem5jB07lrlz5/Lqq68SFhbG2LFjMZvNVFVVceDAAfLy8pg/fz533XVXk877L//yL2RlZfHRRx/x9ddfM3r0aAzDIDs7m927dxMSEsI///M/N+mccoWew5zvm+91TxwiIiINsG9YHR5Qf7G+vV0bW8tPKYFpBqGhoRQVFbF48WIyMjL48MMPMZlMBAUFMWLECOLj4x1jvb292bp1K7NmzWLbtm3k5OTQt29fVq5cSbdu3epNYJYuXQrA1q1bWbduHbW1tQQGBtKvXz8MwyArK4uZM2eyYcMGdu/eTf/+/cnKyuLo0aNNTmAA5s2bx4gRI3jrrbfYsmULp0+fxs/Pj9DQUJKTk3n88cebfM4ePXqwa9cu/vjHP/L555/z3nvvYRgGZrOZmTNnMnv2bPz8/Jp83raiIrH++iDHFZVDO+quvNiVfQnAiQmrsXT5WXOHJyIick0BAQEAFFdaiQx2/ZW0uNLqNE7EzrDZbDZ3B9FSpaamkpiYyPLly102qxT3iYqKIjc3l6b+6O7bt48OHTrQrl27ZorM/TwuVOP7txm0P/M9RvTCuisvZV9i2/gSNV37UBVd/3LYIiJyc12+fNkxjfxWdrXPXqvVyr1DIxjQ6SQZCV5O08hqbTZiP73If52/g+07CrSkchvR2PeFamAaITExEcMwSEhIcHcobZbFYsEwDAzDcKohEme1HbtyasxSarr2gfREWNQb0hOp6dqHU6MXujs8ERERB5PJxCvJ81i/30LspxfJP2zh7CUb+Yfr7q/fb2FuUoqSF3GhKWRXMWDAAJKSkhz37RtPys3n4eHh9L2QhtV6+VIV/YH2gRERkRYvOjqaZcuWMS/5FYZ9+OM+MD2Du7NsWYqWUJZ6aQqZ3BClpaWkpqZec1yXLl2YMWNGs8dTn7YwhUxERFoHTSFzZrVaKSwspLKykoCAACIiInTlpQ1q7PtCV2DkhigtLSUlJeWa48xms9sSGBEREWmZTCYTw4YNu/ZAEZTAyA0SFRXV5KJ6EREREZGmUhG/iIiIiIi0GkpgRERERESk1VACIyIiIiIirYYSGBERERERaTVUxC9tisVicXcIIiIiberzqC09V/nHNPZnRQmMtBnt27enpqYGq9Xq7lBERETaBH32SnPQRpbSZlitVmpra90dhoiICECb2FhZn73SVI15XyiBERERERGRVkNF/CIiIiIi0moogRERERERkVZDCYyIiIiIiLQaSmBERERERKTVUAIjIiIiIiKthvaBERERERG3slqt5OXlUVFRQVBQEMOHD8dkMrk7LGmhlMCI3IL2Hz9LadV5Qvxuo1e3zu4OR0REpEFr165lxoszOXyozNHWo6eZJW8uJi4uzo2RSUulfWBEbiHV52p4Pq2I7QdOOtp+FebPW48NpGun9m6MTERExNXatWuJj4+n488H4xP5KO3uMHP5RBk/FKziQslO0tPTlcSICyUwIreQx5YVsO/4WeaND2dwqC87D57ilcxiftndh4+fjnB3eCIiIg5Wq5XQn/2cqvbd8I+bg2H8WJpts9Vycu18/C5XcrDkgKaTiRMV8V9FamoqhmE4bgkJCU79hYWFjB49Gj8/PwzDICoqyj2BtgEWi8Xpe2EYhrtDanH2Hz9L/vdVzBsfzri7gwjo7MW4u4NIGd+XvP0n2X/8rLtDFBERccjLy+PwoTJ8Ih91Sl4ADMMDn8hHOFxWSl5enpsilJZKNTCNMH78eAYMGEB4eLij7cyZM8TExFBTU8OTTz6Jn58fISEhNyUewzAYOXIkOTk5N+XxmsupU6f4wx/+QEZGBocPH8bHx4eRI0eSkpJC3759ncZ6eHiQlJQE1CWWZWVl9Z2yTSutOg/A4FBfp/YhoV0d/aqHERGRlqKiogKAdneY6+1v5292GidipwSmEWJjY5k8ebJT286dOzlx4gQLFizg5Zdfdk9grVhVVRVDhw5l//79DB06lPHjx1NRUcGaNWv4/PPP2bp1KxERP0558vDwIDk5GYCcnBwlMPUI8bsNgJ0HTzHu7iBH+1cHq536RUREWoKgoLrPqssnyuhw5y9c+i+fLHMaJ2KnBOY6HT16FIDAwEA3R9I6JSUlsX//fmbOnMmiRYsc7fn5+QwfPpwpU6awe/duPDw0y7GxenXrzK/C/HklsxgbNoaEduWrg9UkZf6d4b38dfVFRERalOHDh9Ojp5mqglX11sD8ULCaHuYQhg8f7sYopSXSb4fXwTAMJk2aBEBiYqKjJuPKKV2VlZW8+OKLhIWF0aFDB/z9/Zk4cSLFxcUu58vOzmbKlCn06dMHb29vvL29GTRoEMuWLXMal5OT46j9yM3NdaoHSU1NBSA5OdklFjt7TY99LEBpaSmGYTB58mT27NlDXFwc/v7+GIZBaWmpY1xmZib33Xcfvr6+eHl5ER4ezsKFC7Fardf1GmZkZODh4UFKSopT+9ChQ4mJieG///u/yc3Nva5zt2VvPTaQX3b3YfonRQz5wxamf1LEL7v7sDRhoLtDExERcWIymVjy5mIulOzk5Nr5XCr/jtpL57lU/h0n187nQslOlixepAJ+caErMNchKSmJb775hszMTEd9DOCogSkpKSEqKory8nLuv/9+YmNjqaysZM2aNWzevJktW7Y4TY96/fXXOXDgAJGRkUyYMIHTp0+zadMmnn32Wfbu3eu4QhESEkJSUhIpKSmYzWanaW32GK6X/fH79u3LpEmTqK6upn37umV3Z8+ezYIFCwgODmbixIn4+Piwbds2Zs2aRWFhIatXr27y4x0/fhx/f3+8vb1d+kJDQwHYunUro0aN+oeeV1vTtVN7Pn46QvvAiIhIqxAXF0d6enrdPjB/meVo72EOYYmWUJaG2KRBy5cvtwG25cuXN6lv2LBhNk9PT9sXX3zh1L53715b586dbf369XNq//77713OcfnyZduYMWNsJpPJVlZW5tQH2EaOHFlvzElJSTbAlp2d3aiYDx48aANsgG3u3Lkux3zxxRc2wPbggw/azp0752ivra21PffcczbAlp6eXm8sVxMUFGTz8PCwnT171qUvNjbWBtgeffTReo8dOXKkTT+6IiIitw6LxWLLzs62ffLJJ7bs7GybxWJxd0jSgmkK2Q1WVFTEjh07mDRpEmPGjHHq6927N1OnTmX37t1OU8nsVxyu5OnpyXPPPYfVaiU7O7vZ4w4MDGTOnDku7e+88w4A7733Hrfd9mMRuGEYvPbaaxiGQVpaWpMf78EHH6S2ttZlCtlXX33F+vXrATh9+nSTzysiIiKtj8lkIioqiscee4yoqChNG5Or0hSyG6ygoACAY8eOOVbNutKePXscX+3LMp89e5aFCxeSkZFBSUkJ586dczrGvmBAc+rfv79jytiVCgoK6NSpEx988EG9x3Xs2NHxnJoiJSWFzz//nIULF5Kfn09kZCQVFRWkp6fzy1/+km+//Vb/eYmIiLQRVquVvLw8KioqCAoKYvjw4fo9QBqkBOYGq66uW7J2w4YNbNiwocFx9iSlpqaGqKgodu3axcCBAx17ynh6elJaWsqKFSu4dOlSs8fdrVu3eturq6uxWCwuV0qu9NOEqzGCg4PZuXMnSUlJfP7553z11Vf06NGDefPmERISQkJCAnfccUeTz9smVe6B6u+h688gwHUZShERkZZs7dq1/O7FFyg9dMTRFtIzmEVvLlUNjNRLCcwN5uPjA8Dbb7/N9OnTrzk+MzOTXbt28cwzz/D+++879X366aesWLGiSY9vX3bYYrG49J05c6bB4xra2d7HxwfDMDh58mST4miMO++8kz//+c8u7fYrV4MGDbrhj3lLObEP1s+Asi9/bDPfC49+DJ383BaWiIhIY61du5b4+Hge6uVJ2tO3ER5gorjSyh+3Hyc+Pp50FfJLPVQDc4PZVxfLz89v1PiSkhIAHn74YZe+vLy8eo/x8PBocPliX9+6XdjLy8td+oqKihoV05UiIiKoqqpi//79TT72elitVj799FM8PT2ZOHHiTXnMVqHmnOvt3yPg+N/hkVT43b66r8f/DumTnceJiIi0QFarld+9+AIP9fIkI8GLyGBPvNsbRAZ7kvFrLx7q7clLM2dc95YNcutSAnODDRkyhIiICNLS0vjss89c+mtra532NzGbzQBs377daVxubq7LFRm7rl27cuTIkXr77FctPvroI2prax3t+fn5rFy5smlPBnj++ecBmDJlClVVVS79x44d47vvvmvyeS9fvsyFCxec2mpra3nppZfYu3cv/+///T+6d+/e5PPesv7Y3fVmq4WYJdB3AnTuVvf1oTfh4DbncSIiIi1QXl4epYeOMHt4Ozx+MhPEwzD4/b3tOFh2uME/6ErbpSlkzSAtLY1Ro0aRkJDAkiVLuOeee/Dy8uLQoUPk5+dz4sQJLl68CEBMTAwhISG88cYbFBcXEx4ezt69e1m/fj2xsbGsWbPG5fyjR49m1apVxMfHM3DgQEwmE+PGjaNfv35ERkYydOhQtm7dytChQxkxYgRlZWVkZWURExPDunXrmvRcxo4dy9y5c3n11VcJCwtj7NixmM1mqqqqOHDgAHl5ecyfP5+77rqrSec9fvw4ffv25f777yc0NJSamho2b97Mnj17GDduHAsWLGjS+dqsnsOc75vvdU8cIiIiTVRRUQFAeED9xfr2dvs4ETslMM0gNDSUoqIiFi9eTEZGBh9++CEmk4mgoCBGjBhBfHy8Y6y3tzdbt25l1qxZbNu2jZycHPr27cvKlSvp1q1bvQnM0qVLgbqNHtetW0dtbS2BgYH069cPwzDIyspi5syZbNiwgd27d9O/f3+ysrI4evRokxMYgHnz5jFixAjeeusttmzZwunTp/Hz8yM0NJTk5GQef/zxJp/z9ttvZ/z48Xz55ZesX7+edu3aER4ezvvvv8+UKVMctTzyv2b/ZCW6E/vg/Sg4tKPuyoudvR5mag7c0ftmRSciItJkQUFBABRXWokMdv2VtLjS6jROxM6w2Ww2dwfRUqWmppKYmMjy5cuddr0X94qKiiI3N5c2/6O7PBpO7IXof6278lL2JWycBYH94KkMd0cnIiJyVVarlbCfhdCv43Eyfu3lNI2s1mYj9rOLFF8MZH/JQS2pLE70Z+5GSExMxDAMEhIS3B1Km2WxWDAMA8MwnGqI2rRHP65LVtITYVHvuq+B/WBi/Xv2iIiItCQmk4lFby5l/T4LsZ9dJP+whbOXbOQfrru/fp+FhYuXKHkRF5pCdhUDBgwgKSnJcd++8aTcfB4eHk7fC6FuqeSnMrQPjIiItFpxcXGkp6fzuxdfYNiHPy5QFGruQXr6Ei2hLPXSFDK5IUpLS0lNTb3muC5dujBjxoxmj0dERERaD6vVSl5eHhUVFQQFBTF8+HBdeZEGKYGRGyInJ4dRo0Zdc5zZbKa0tLT5AxIRERGRW5ISGBERERERaTVUxC8iIiIiIq2GEhgREREREWk1lMCIiIiIiEiroQRGRERERERaDe0DI22G1WqltrbW3WGIiIgA0K5dO3eH0Oz02StN1Zj3hRIYaTMOHz5MTU2Nu8MQEREBoHfv3u4Oodnps1eaqjHvCyUw0mbU1NRgMpnw9NSPvYiIuJfFYnF3CDeFPnulKRr7vtBPk7Qpnp6ebeKSvYiISEuhz1650VTELyIiIiIirYYSGBERERERaTWUwIiIiIiISKuhBEZERERERFoNJTAiIiIiItJqKIEREREREZFWQ8soi4iIiIhbWa1WCgsLqaysJCAggIiICEwmk7vDkhZKCYzILehg1QWOnKkh+Pb2hPp1dHc4IiIiDdq4cSNJySkcLT/iaOt+ZzApyUlER0e7MTJpqZTAiNxCTl+wkLSplJ2HzzraBvfoTMrYELp01NtdRERalo0bNzJt2jQ6/nwwgU/MoN0dZi6fKON0wSqmTZvGsmXLlMSIC8Nms9ncHYTIzbBv3z46dOhwy+4GfOGylX/6j+85eOoSr47vx+BQX3YePMXczN2E+XmxJDbM3SGKiMj/unz5Mmaz2d1hNLurffZarVYihw7jdMfu+MfNwTB+LM222Wo5uXY+XS5WULDjS00nayMa+75oU0X8qampGIbhuCUkJDj1FxYWMnr0aPz8/DAMg6ioKPcEKvV64oknnL5/qamp7g6pRbnv3W/5+sj/8Or4foy7O4iAzl6MuzuIeePD+erQWQ5WXXB3iCIiIg6FhYUcLT+CT+SjTskLgGF44BP5CEePHKawsNBNEUpL1SbnlIwfP54BAwYQHh7uaDtz5gwxMTHU1NTw5JNP4ufnR0hIyE2JxzAMRo4cSU5Ozk15vOZw/vx53n33Xb7++mt27drFvn37sNlsHDx48Kqv4/79+/mXf/kXsrOz+Z//+R969erFtGnT+O1vf4uHh/N/ZnFxcYSFhfHNN9+QmZnZzM+o9Roc6ut0f0hoVwCOnKlRPYyIiLQYlZWVALS7o/6/uLfzNzuNE7FrkwlMbGwskydPdmrbuXMnJ06cYMGCBbz88svuCawVq6ys5KWXXgLAbDbj6+tLdXX1VY/57//+b4YNG8b58+d59NFHufPOO/n888/5f//v//Htt9+ybNkyp/FxcXHExcWRmpqqBKYeH/y6N09/to+dB08x7u4gR/tXB+u+D8G3t3dXaCIiIi4CAgIAuHyijA53/sKl//LJMqdxInZtagrZ1Rw9ehSAwMBAN0fSOvn7+/PFF19QVVVFaWkpgwcPvuYxv/nNbzhz5gwZGRn85S9/4fXXX+frr7/mvvvu4/333yc7O/smRH7ruKtbJwb36MzczN2s//YolWcvsv7bo7ySWcyQnp119UVERFqUiIgIut8ZzA8Fq7DZap36bLZafihYTffgHkRERLgpQmmplMBQN4Vr0qRJACQmJjpqLK6c0lVZWcmLL75IWFgYHTp0wN/fn4kTJ1JcXOxyvuzsbKZMmUKfPn3w9vbG29ubQYMGuVxRyMnJwTAMAHJzc+ut70hOTnaJxc5e03NlLUhpaSmGYTB58mT27NlDXFwc/v7+GIZBaWmpY1xmZib33Xcfvr6+eHl5ER4ezsKFC7Fardf1Gnp7ezNmzBi6du3aqPH79u1j27ZtjBo1yml1kXbt2vGHP/wBgPfff/+6YmnLUsaGEObnxfRPihjyhy1M/6SIMD8vkh8IcXdoIiIiTkwmEynJSVwo2cnJtfO5VP4dtZfOc6n8O06unc+Fkp2kJL2iAn5x0SankP1UUlKSo67CXh8DOGo3SkpKiIqKory8nPvvv5/Y2FgqKytZs2YNmzdvZsuWLU5/HXj99dc5cOAAkZGRTJgwgdOnT7Np0yaeffZZ9u7dy6JFixznT0pKIiUlBbPZ7DStzR7D9bI/ft++fZk0aRLV1dW0b183hWj27NksWLCA4OBgJk6ciI+PD9u2bWPWrFkUFhayevXqf+ixG8OekN1///0ufUOGDKFLly7k5uY2exy3mi4dPVkSG6Z9YEREpFWIjo5m2bJldfvA/GWWo717cA+WagllaYASGOquctjrKuqrj3nqqac4duwYmzdvZsyYMY72OXPmMGjQIKZOncq3337raH/33XcJDQ11OofFYiE6OpqlS5fywgsv0LNnT0JCQkhOTiYlJcXx7xvlyy+/ZO7cucybN8+p/a9//SsLFizgwQcfJD09ndtuuw0Am83Gb3/7W/70pz+xZs0aJk6ceMNiqc/+/fsB6NWrl0ufYRiEhYXxn//5n5w/f94RozReqF9HJS4iItIqREdH88ADD1BYWEhlZSUBAQFEREToyos0SFPIrqGoqIgdO3YwadIkp+QFoHfv3kydOpXdu3c7TSX7afIC4OnpyXPPPYfVar0ptR2BgYHMmTPHpf2dd94B4L333nNKDAzD4LXXXsMwDNLS0po9vjNnzgBw++2319vv4+PjNE5ERERuXSaTiWHDhhEbG8uwYcOUvMhV6QrMNRQUFABw7Nixeq+Q7Nmzx/HVvizz2bNnWbhwIRkZGZSUlHDu3DmnY+wLBjSn/v37O6aMXamgoIBOnTrxwQcf1Htcx44dHc9JRERE5GawWq26AiONpgTmGuxLAW/YsIENGzY0OM6epNTU1BAVFcWuXbsYOHCgY08ZT09PSktLWbFiBZcuXWr2uLt161Zve3V1NRaLhZSUlAaP/WnC1RzsV14ausLyww8/AD9eiZGGeZ46gOnMIay398TiG+bucERERJpk48aNzEt+hcPlFY62HncG8UryPNXASL2UwFyD/Rfot99+m+nTp19zfGZmJrt27eKZZ55xWUXr008/ZcWKFU16fPtmjhaLxaXvatOr7Kub/ZSPjw+GYXDy5MkmxXGj2Wtf7LUwV7LZbBw4cIDu3bvTqVOnmx1aq2E69T1dtifToWKno+1S0GBOjVlKrZfvVY4UERFpGTZu3Mi0adN4qJcnq56+jfAAE8WVVv6Yd5Jp06axTIX8Ug/VwFyDfXWx/Pz8Ro0vKSkB4OGHH3bpy8vLq/cYDw+PBpcv9vWt+0W0vLzcpa+oqKhRMV0pIiKCqqqqehOHmykqKgqAL774wqXvq6++4vTp04wcOfImR9VyGZfPu9y6pcfQ4dR+eCQVfrcPHkmlw6n9+P5tpstYERGRlsZqtTIv+RUe6uVJRoIXkcGeeLc3iAyuu/9QL09eTUm67i0e5NalBOYahgwZQkREBGlpaXz22Wcu/bW1tU7L/ZrNZgC2b9/uNC43N7fBfU26du3KkSNH6u0bNGgQAB999BG1tT9u8pSfn8/KlSub9mSA559/HoApU6ZQVVXl0n/s2DG+++67Jp+3qXr37s2IESPIzs5m48aNjvbLly87Fh+YOnVqs8fRWgQtv8flhq0WYpZA3wnQuVvd14fepMPRAtexIiIiLUxhYSGHyyuYPbwdHj+ZOeJhGPz+V+04dOQohYWFbopQWipNIWuEtLQ0Ro0aRUJCAkuWLOGee+7By8uLQ4cOkZ+fz4kTJ7h48SIAMTExhISE8MYbb1BcXEx4eDh79+5l/fr1xMbGsmbNGpfzjx49mlWrVhEfH8/AgQMxmUyMGzeOfv36ERkZydChQ9m6dStDhw5lxIgRlJWVkZWVRUxMDOvWrWvScxk7dixz587l1VdfJSwsjLFjx2I2m6mqquLAgQPk5eUxf/587rrrria/Ti+99JJjatru3bsdbd7e3gC8/PLL/OIXv3CMf/fddxk2bBgTJkzg0UcfpXv37mzatIlvv/2WZ555hlGjRjU5hjan5zDn++Z73ROHiIhIE1VWVgIQHlB/sb693T5OxE4JTCOEhoZSVFTE4sWLycjI4MMPP8RkMhEUFMSIESOIj493jPX29mbr1q3MmjWLbdu2kZOTQ9++fVm5ciXdunWrN4FZunQpAFu3bmXdunXU1tYSGBhIv379MAyDrKwsZs6cyYYNG9i9ezf9+/cnKyuLo0ePNjmBAZg3bx4jRozgrbfeYsuWLZw+fRo/Pz9CQ0NJTk7m8ccfv67XKT09nbKyMqe2K5/v5MmTnRKYX/7yl3z11Vf8y7/8C59//jn/8z//Q1hYGG+99Rb/9//+3+uK4VZVkfi1033P099zx7pH4NCOuisvdmVfAnBiwmosXX52M0MUERFpkoCAAACKK61EBrv+SlpcaXUaJ2Jn2Gw2m7uDuFlSU1NJTExk+fLlLptVSutxvd/Hffv20aFDB9q1a9d8wd1Efv/xFO3PfI8RvbDuykvZl9g2vkRN1z5URde/TLaIiLQMly9fdkw7v5Vd7bPXarVy79AIBnQ6SUaCl9M0slqbjdhPL/Jf5+9g+44CLancRjT2fdEma2ASExMxDIOEhAR3hyJN8MQTT2AYBomJie4OpUU4NWYpNV37QHoiLOoN6YnUdO3DqdEL3R2aiIjINZlMJl5Jnsf6/RZiP71I/mELZy/ZyD9cd3/9fgtzk1KUvIiLNjWFbMCAASQlJTnu2zeelNYhLi6OsLAf9zkZMGCA+4JpAWq9fKmK/kD7wIiISKsVHR3NsmXLmJf8CsM+/HEfmJ7B3Vm2LEVLKEu92tQUMmm80tJSUlNTrzmuS5cuzJgxo9njuRFutSlkIiLSemkKmTOr1UphYSGVlZUEBAQQERGhKy9tUGPfF23qCow0XmlpKSkpKdccZzabW00CIyIiIi2TyWRi2LBh1x4oghIYaUBUVBS6OCciIiIiLU2bLOIXEREREZHWSQmMiIiIiIi0GkpgRERERESk1VACIyIiIiIirYaK+KVNsVgs7g5BRESkTX0etaXnKv+Yxv6sKIGRNqN9+/bU1NRgtVrdHYqIiEiboM9eaQ7ayFLaDKvVSm1trbvDEBERAWgTGyvrs1eaqjHvCyUwIiIiIiLSaqiIX0REREREWg0lMCIiIiIi0moogRERERERkVZDCYyIiIiIiLQaSmBERERERKTV0D4wIiIiIuJWVquVvLw8KioqCAoKYvjw4ZhMJneHJS2UEhiRW9D+42cprTpPiN9t9OrW2d3hiIiINGjt2rXMeHEmhw+VOdp69DSz5M3FxMXFuTEyaam0D4zILaT6XA3PpxWx/cBJR9uvwvx567GBdO3U3o2RiYiIuFq7di3x8fF0/PlgfCIfpd0dZi6fKOOHglVcKNlJenq6khhxoQRG5Bby2LIC9h0/y7zx4QwO9WXnwVO8klnML7v78PHTEe4OT0RExMFqtRL6s59T1b4b/nFzMIwfS7NttlpOrp2P3+VKDpYc0HQycdKmivhTU1MxDMNxS0hIcOovLCxk9OjR+Pn5YRgGUVFR7glU6vXEE084ff9SU1PdHVKLsv/4WfK/r2Le+HDG3R1EQGcvxt0dRMr4vuTtP8n+42fdHaKIiIhDXl4ehw+V4RP5qFPyAmAYHvhEPsLhslLy8vLcFKG0VG2yBmb8+PEMGDCA8PBwR9uZM2eIiYmhpqaGJ598Ej8/P0JCQm5KPIZhMHLkSHJycm7K4zWH8+fP8+677/L111+za9cu9u3bh81m4+DBgw2+jv/xH//BF198wa5du/jmm284f/48SUlJJCcn1zs+Li6OsLAwvvnmGzIzM5vvybRSpVXnARgc6uvUPiS0q6Nf9TAiItJSVFRUANDuDnO9/e38zU7jROzaZAITGxvL5MmTndp27tzJiRMnWLBgAS+//LJ7AmvFKisreemllwAwm834+vpSXV191WMWLVpEbm4uPj4+dO/enQMHDlx1fFxcHHFxcaSmpiqBqUeI320A7Dx4inF3BznavzpY7dQvIiLSEgQF1X1WXT5RRoc7f+HSf/lkmdM4Ebs2NYXsao4ePQpAYGCgmyNpnfz9/fniiy+oqqqitLSUwYMHX/OYV199lX379nH69GleffXVmxDlra1Xt878KsyfVzKLWf/tUSrPXmT9t0dJyvw7w3v56+qLiIi0KMOHD6dHTzM/FKzCZqt16rPZavmhYDU9zCEMHz7cTRFKS6UEhropXJMmTQIgMTHRUWNx5ZSuyspKXnzxRcLCwujQoQP+/v5MnDiR4uJil/NlZ2czZcoU+vTpg7e3N97e3gwaNIhly5Y5jcvJycEwDAByc3Prre9ITk52icXOXtNzZS1IaWkphmEwefJk9uzZQ1xcHP7+/hiGQWlpqWNcZmYm9913H76+vnh5eREeHs7ChQuxWq3X9Rp6e3szZswYunbt2uhjhg8fTq9evRyvgfzj3npsIL/s7sP0T4oY8octTP+kiF9292FpwkB3hyYiIuLEZDKx5M3FXCjZycm187lU/h21l85zqfw7Tq6dz4WSnSxZvEgF/OKiTU4h+6mkpCRHXYW9PgZw1G6UlJQQFRVFeXk5999/P7GxsVRWVrJmzRo2b97Mli1biIj4cYWn119/nQMHDhAZGcmECRM4ffo0mzZt4tlnn2Xv3r0sWrTIcf6kpCRSUlIwm81O09rsMVwv++P37duXSZMmUV1dTfv2dcvozp49mwULFhAcHMzEiRPx8fFh27ZtzJo1i8LCQlavXv0PPba4T9dO7fn46QjtAyMiIq1CXFwc6enpdfvA/GWWo72HOYQlWkJZGqAEhrqrHPa6ivrqY5566imOHTvG5s2bGTNmjKN9zpw5DBo0iKlTp/Ltt9862t99911CQ0OdzmGxWIiOjmbp0qW88MIL9OzZk5CQEJKTk0lJSXH8+0b58ssvmTt3LvPmzXNq/+tf/8qCBQt48MEHSU9P57bb6uoibDYbv/3tb/nTn/7EmjVrmDhx4g2LRW6+Xt06K3EREZFWIS4ujvHjx5OXl0dFRQVBQUEMHz5cV16kQZpCdg1FRUXs2LGDSZMmOSUvAL1792bq1Kns3r3baSrZT5MXAE9PT5577jmsVivZ2dnNHndgYCBz5sxxaX/nnXcAeO+99xzJC9RNo3vttdcwDIO0tLRmj09ERETEzmQyERUVxWOPPUZUVJSSF7kqXYG5hoKCAgCOHTtW7xWSPXv2OL7al2U+e/YsCxcuJCMjg5KSEs6dO+d0jH3BgObUv39/x5SxKxUUFNCpUyc++OCDeo/r2LGj4zmJiIiI3AxWq1VXYKTRlMBcg30p4A0bNrBhw4YGx9mTlJqaGqKioti1axcDBw507Cnj6elJaWkpK1as4NKlS80ed7du3eptr66uxmKxkJKS0uCxP024pIWr3APV30PXn0GA6zKUIiIiLdnatWv53YsvUHroiKMtpGcwi95cqhoYqZcSmGvw8fEB4O2332b69OnXHJ+ZmcmuXbt45plneP/99536Pv30U1asWNGkx/fwqJvlZ7FYXPrOnDnT4HENrezl4+ODYRicPHmySXFIC3RiH6yfAWVf/thmvhce/Rg6+bktLBERkcZau3Yt8fHxPNTLk7SnbyM8wERxpZU/bj9OfHw86Srkl3qoBuYa7KuL5efnN2p8SUkJAA8//LBLX15eXr3HeHh4NLh8sa9v3a7q5eXlLn1FRUWNiulKERERVFVVsX///iYfK25Uc8719u8RcPzv8Egq/G5f3dfjf4f0ya5jRUREWhir1crvXnyBh3p5kpHgRWSwJ97tDSKDPcn4tRcP9fbkpZkzrnuLB7l1KYG5hiFDhhAREUFaWhqfffaZS39tbS25ubmO+2azGYDt27c7jcvNzXW5ImPXtWtXjhw5Um/foEGDAPjoo4+orf1xk6f8/HxWrlzZtCcDPP/88wBMmTKFqqoql/5jx47x3XffNfm80sz+2N31ZquFmCXQdwJ07lb39aE34eA217EiIiItTF5eHqWHjjB7eDs8fjJzxMMw+P297ThYdrjBPwBL26UpZI2QlpbGqFGjSEhIYMmSJdxzzz14eXlx6NAh8vPzOXHiBBcvXgQgJiaGkJAQ3njjDYqLiwkPD2fv3r2sX7+e2NhY1qxZ43L+0aNHs2rVKuLj4xk4cCAmk4lx48bRr18/IiMjGTp0KFu3bmXo0KGMGDGCsrIysrKyiImJYd26dU16LmPHjmXu3Lm8+uqrhIWFMXbsWMxmM1VVVRw4cIC8vDzmz5/PXXfd1eTX6aWXXnJMTdu9e7ejzdvbG4CXX36ZX/zixxqNjIwMMjIyADh48KCjzb7h5q9+9SueeeaZJsfRpvQc5nzffK974hAREWmiiooKAMID6i/Wt7fbx4nYKYFphNDQUIqKili8eDEZGRl8+OGHmEwmgoKCGDFiBPHx8Y6x3t7ebN26lVmzZrFt2zZycnLo27cvK1eupFu3bvUmMEuXLgVg69atrFu3jtraWgIDA+nXrx+GYZCVlcXMmTPZsGEDu3fvpn///mRlZXH06NEmJzAA8+bNY8SIEbz11lts2bKF06dP4+fnR2hoKMnJyTz++OPX9Tqlp6dTVlbm1Hbl8508ebJTAvPNN9+41AT913/9F//1X//luK8E5n/N/snKdSf2wftRcGhH3ZUXO3s9zNQcuKP3zYpORESkyYKCggAorrQSGez6K2lxpdVpnIidYbPZbO4O4mZJTU0lMTGR5cuXu2xWKa2Hvo//a3k0nNgL0f9ad+Wl7EvYOAsC+8FTGe6OTkRE5KqsVithPwuhX8fjZPzay2kaWa3NRuxnFym+GMj+koNaUlmctMkamMTERAzDICEhwd2hSBM88cQTGIZBYmKiu0NpGR79uC5ZSU+ERb3rvgb2g4n17/EjIiLSkphMJha9uZT1+yzEfnaR/MMWzl6ykX+47v76fRYWLl6i5EVctKkrMN98842j5gIgPDzcafqXtGxr167l22+/ddyPjY1lwIAB7guopdA+MCIi0orVtw9MqLkHCxcv0RLKUq82lcBI45WWlpKamnrNcV26dGHGjBnNHo+IiIjcuqxWK3l5eVRUVBAUFMTw4cN15UUapARG6pWTk8OoUaOuOc5sNjtWDRMRERERaW5KYEREREREpNVok0X8IiIiIiLSOimBERERERGRVkMJjIiIiIiItBpKYEREREREpNXwdHcAIjeL1WqltrbW3WGIiIgA0K5dO3eH0Oz02StN1Zj3hRIYaTMOHz5MTU2Nu8MQEREBoHfv3u4Oodnps1eaqjHvCyUw0mbU1NRgMpnw9NSPvYiIuJfFYnF3CDeFPnulKRr7vtBPk7Qpnp6ebeKSvYiISEuhz1650VTELyIiIiIirYYSGBERERERaTWUwIiIiIiISKuhBEZERERERFoNJTAiIiIiItJqaBUyEREREXErq9VKYWEhlZWVBAQEEBERgclkcndY0kIpgRG5BR2susCRMzUE396eUL+O7g5HRESkQRs3biQpOYWj5Uccbd3vDCYlOYno6Gg3RiYtlRIYkVvI6QsWkjaVsvPwWUfb4B6dSRkbQpeOeruLiEjLsnHjRqZNm0bHnw8m8IkZtLvDzOUTZZwuWMW0adNYtmyZkhhxYdhsNpu7gxC5Gfbt20eHDh1u2c20Lly28k//8T0HT13i1fH9GBzqy86Dp5ibuZswPy+WxIa5O0QREflfly9fxmw2uzuMZne1z16r1Urk0GGc7tgd/7g5GMaPpdk2Wy0n186ny8UKCnZ8qelkbURj3xdtqog/NTUVwzAct4SEBKf+wsJCRo8ejZ+fH4ZhEBUV5Z5ApV5PPPGE0/cvNTXV3SG1KPe9+y1fH/kfXh3fj3F3BxHQ2Ytxdwcxb3w4Xx06y8GqC+4OUURExKGwsJCj5UfwiXzUKXkBMAwPfCIf4eiRwxQWFropQmmp2uSckvHjxzNgwADCw8MdbWfOnCEmJoaamhqefPJJ/Pz8CAkJuSnxGIbByJEjycnJuSmP1xzOnz/Pu+++y9dff82uXbvYt28fNpuNgwcP1vs6VlVVsWbNGtavX09xcTHl5eV07tyZwYMHM2PGDB544AGXY+Li4ggLC+Obb74hMzPzJjyr1mlwqK/T/SGhXQE4cqZG9TAiItJiVFZWAtDujvr/4t7O3+w0TsSuTSYwsbGxTJ482alt586dnDhxggULFvDyyy+7J7BWrLKykpdeegkAs9mMr68v1dXVDY5fvXo1v/nNb7jzzjsZPXo0d955J0eOHGHNmjVs2rSJf/3Xf3Wczy4uLo64uDhSU1OVwNTjg1/35unP9rHz4CnG3R3kaP/qYN33Ifj29u4KTURExEVAQAAAl0+U0eHOX7j0Xz5Z5jROxK5NTSG7mqNHjwIQGBjo5khaJ39/f7744guqqqooLS1l8ODBVx3fu3dv1q9fz6FDh/joo49YsGABH3/8MUVFRdx+++3Mnj3b8T2RxrmrWycG9+jM3MzdrP/2KJVnL7L+26O8klnMkJ6ddfVFRERalIiICLrfGcwPBauw2Wqd+my2Wn4oWE334B5ERES4KUJpqZTAUDeFa9KkSQAkJiY6aiyunNJVWVnJiy++SFhYGB06dMDf35+JEydSXFzscr7s7GymTJlCnz598Pb2xtvbm0GDBrFs2TKncTk5ORiGAUBubm699R3JyckusdjZa3qurAUpLS3FMAwmT57Mnj17iIuLw9/fH8MwKC0tdYzLzMzkvvvuw9fXFy8vL8LDw1m4cCFWq/W6XkNvb2/GjBlD165dGzV+9OjRjBs3Dg8P5x/BPn368Otf/5rLly+zY8eO64qlLUsZG0KYnxfTPyliyB+2MP2TIsL8vEh+IMTdoYmIiDgxmUykJCdxoWQnJ9fO51L5d9ReOs+l8u84uXY+F0p2kpL0igr4xUWbnEL2U0lJSY66Cnt9DOCo3SgpKSEqKory8nLuv/9+YmNjqaysZM2aNWzevJktW7Y4/XXg9ddf58CBA0RGRjJhwgROnz7Npk2bePbZZ9m7dy+LFi1ynD8pKYmUlBTMZrPTtDZ7DNfL/vh9+/Zl0qRJVFdX07593RSi2bNns2DBAoKDg5k4cSI+Pj5s27aNWbNmUVhYyOrVq/+hx/5H2Vcq8fTUj2dTdenoyZLYMO0DIyIirUJ0dDTLli2r2wfmL7Mc7d2De7BUSyhLA/QbInVXOex1FfXVxzz11FMcO3aMzZs3M2bMGEf7nDlzGDRoEFOnTuXbb791tL/77ruEhoY6ncNisRAdHc3SpUt54YUX6NmzJyEhISQnJ5OSkuL4943y5ZdfMnfuXObNm+fU/te//pUFCxbw4IMPkp6ezm233QaAzWbjt7/9LX/6059Ys2YNEydOvGGxNMXZs2dJT0/Hy8uL4cOHuyWGW0GoX0clLiIi0ipER0fzwAMPUFhYSGVlJQEBAUREROjKizRIU8iuoaioiB07djBp0iSn5AXq6jimTp3K7t27naaS/TR5gbqrCc899xxWq5Xs7OxmjzswMJA5c+a4tL/zzjsAvPfee47kBeqm0b322msYhkFaWlqzx9eQ5557juPHjzN79mz8/PzcFoeIiIjcPCaTiWHDhhEbG8uwYcOUvMhV6QrMNRQUFABw7Nixeq+Q7Nmzx/HVvizz2bNnWbhwIRkZGZSUlHDu3DmnY25GcXr//v0dU8auVFBQQKdOnfjggw/qPa5jx46O53SzzZ49m08++YSxY8cye/Zst8QgIiIiN5/VatUVGGk0JTDXYF8KeMOGDWzYsKHBcfYkpaamhqioKHbt2sXAgQMde8p4enpSWlrKihUruHTpUrPH3a1bt3rbq6ursVgspKSkNHjsTxOumyElJYUFCxYwevRo1q5dq/+0msDz1AFMZw5hvb0nFt8wd4cjIiLSJBs3bmRe8iscLq9wtPW4M4hXkuepBkbqpQTmGnx8fAB4++23mT59+jXHZ2ZmsmvXLp555hnef/99p75PP/2UFStWNOnx7at0WSwWl74zZ840eJx9dbOf8vHxwTAMTp482aQ4mlNKSgrJyclERUXxH//xH3TsqNqNxjCd+p4u25PpULHT0XbpzmGcum8htV6+VzlSRESkZdi4cSPTpk3joV6erHr6NsIDTBRXWvlj3kmmTZvGMhXySz1UA3MN9tXF8vPzGzW+pKQEgIcfftilLy8vr95jPDw8Gly+2Ne37hfR8vJyl76ioqJGxXSliIgIqqqq2L9/f5OPbQ7JyckkJyczcuRINmzY4FSXI3WMy+frvXVLj6HDqf3wSCr8bh88kkr7U/vw/dtMp3EiIiItkdVqZV7yKzzUy5OMBC8igz3xbm8QGVx3/6FenryaknTdWzzIrUsJzDUMGTKEiIgI0tLS+Oyzz1z6a2tryc3Nddw3m80AbN++3Wlcbm6uyxUZu65du3LkyJF6+wYNGgTARx99RG3tj5s85efns3LlyqY9GeD5558HYMqUKVRVVbn0Hzt2jO+++67J570e9iWkhw8fruTlKoKW31PvDVstxCyBvhOgczfoOwEj+l/pcLTAeZyIiEgLVFhYyOHyCmYPb4fHT2aOeBgGv/9VOw4dOUphYaGbIpSWSlPIGiEtLY1Ro0aRkJDAkiVLuOeee/Dy8uLQoUPk5+dz4sQJLl68CEBMTAwhISG88cYbFBcXEx4ezt69e1m/fj2xsbGsWbPG5fyjR49m1apVxMfHM3DgQEwmE+PGjaNfv35ERkYydOhQtm7dytChQxkxYgRlZWVkZWURExPDunXrmvRcxo4dy9y5c3n11VcJCwtj7NixmM1mqqqqOHDgAHl5ecyfP5+77rqrya/TSy+95Jiatnv3bkebt7c3AC+//DK/+MUvgLpNOOfNm4enpydDhgzhX//1X13OFxUVRVRUVJPjaFN6DnO+b77XPXGIiIg0UWVlJQDhAfXXvdrb7eNE7JTANEJoaChFRUUsXryYjIwMPvzwQ0wmE0FBQYwYMYL4+HjHWG9vb7Zu3cqsWbPYtm0bOTk59O3bl5UrV9KtW7d6E5ilS5cCsHXrVtatW0dtbS2BgYH069cPwzDIyspi5syZbNiwgd27d9O/f3+ysrI4evRokxMYgHnz5jFixAjeeusttmzZwunTp/Hz8yM0NJTk5GQef/zx63qd0tPTKSsrc2q78vlOnjzZkcCUlpYCdbU99o0966MEBioSv3Zp8zz9PXesewQO7ai7AmNX9iUAJyasxtLlZzcrRBERkSYLCAgAoLjSSmSw66+kxZVWp3EidobNZrO5O4ibJTU1lcTERJYvX+6yWaW0Htf7fdy3bx8dOnSgXbt2zRfcTeT3H0/R/sz3GNEL6668lH2JbeMsarr2piq6/mWyRUSkZbh8+bJj2vmt7GqfvVarlXuHRjCg00kyErycppHV2mzEfnqR/zp/B9t3FGh10jaise+LNlkDk5iYiGEYJCQkuDsUaYInnngCwzBITEx0dygtwqkxS6np2gfSE2FRb0hPpKZrb06NXuju0ERERK7JZDLxSvI81u+3EPvpRfIPWzh7yUb+4br76/dbmJuUouRFXLSpKWQDBgwgKSnJcd++8aS0DnFxcYSF/bjPyYABA9wXTAtQ6+VLVfQH2gdGRERarejoaJYtW8a85FcY9uGP+8D0DO7OsmUpWkJZ6tWmppBJ45WWlpKamnrNcV26dGHGjBnNHs+NcKtNIRMRkdZLU8icWa1WCgsLqaysJCAggIiICF15aYMa+75oU1dgpPFKS0tJSUm55jiz2dxqEhgRERFpmUwmE8OGDbv2QBGUwEgDoqKi0MU5EREREWlp2mQRv4iIiIiItE5KYEREREREpNVQAiMiIiIiIq2GEhgREREREWk1lMCIiIiIiEiroVXIpE2xWCzuDkFERKRNfR61pecq/5jG/qwogZE2o3379tTU1GC1Wt0dioiISJugz15pDoZNm31IG2G1WqmtrXV3GCIiIgDX3J3+VqDPXmmqxrwvlMCIiIiIiEiroSJ+ERERERFpNZTAiIiIiIhIq6EERkREREREWg0lMCIiIiIi0moogRERERERkVZD+8CIiIiIiFtZrVby8vKoqKggKCiI4cOHYzKZ3B2WtFBKYERuQfuPn6W06jwhfrfRq1tnd4cjIiLSoLVr1zLjxZkcPlTmaOvR08ySNxcTFxfnxsikpdI+MCK3kOpzNTyfVsT2Aycdbb8K8+etxwbStVN7N0YmIiLiau3atcTHx9Px54PxiXyUdneYuXyijB8KVnGhZCfp6elKYsSFEhiRW8hjywrYd/ws88aHMzjUl50HT/FKZjG/7O7Dx09HuDs8ERERB6vVSujPfk5V+274x83BMH4szbbZajm5dj5+lys5WHJA08nESZsq4k9NTcUwDMctISHBqb+wsJDRo0fj5+eHYRhERUW5J1Cp1xNPPOH0/UtNTXV3SC3K/uNnyf++innjwxl3dxABnb0Yd3cQKeP7krf/JPuPn3V3iCIiIg55eXkcPlSGT+SjTskLgGF44BP5CIfLSsnLy3NThNJStckamPHjxzNgwADCw8MdbWfOnCEmJoaamhqefPJJ/Pz8CAkJuSnxGIbByJEjycnJuSmP1xzOnz/Pu+++y9dff82uXbvYt28fNpuNgwcP1vs62mw2/umf/omdO3eyb98+qquruf322/n5z3/O008/zVNPPUW7du2cjomLiyMsLIxvvvmGzMzMm/TMWo/SqvMADA71dWofEtrV0a96GBERaSkqKioAaHeHud7+dv5mp3Eidm0ygYmNjWXy5MlObTt37uTEiRMsWLCAl19+2T2BtWKVlZW89NJLAJjNZnx9famurm5wvNVq5e2332bQoEGMGzeOO+64g1OnTrFp0yaeeeYZVq9ezcaNG/Hw+PEvMnFxccTFxZGamqoEph4hfrcBsPPgKcbdHeRo/+pgtVO/iIhISxAUVPdZdflEGR3u/IVL/+WTZU7jROzaZAJTn6NHjwIQGBjo5khaJ39/f7744gvuueceunbtytixY9m8eXOD4z09PTl9+jReXl5O7RaLhfvvv5/Nmzfz+eefM27cuOYO/ZbRq1tnfhXmzyuZxdiwMSS0K18drCYp8+8M7+Wvqy8iItKiDB8+nB49zVQVrKq3BuaHgtX0MIcwfPhwN0YpLVGbqoFpiGEYTJo0CYDExERHjcWVU7oqKyt58cUXCQsLo0OHDvj7+zNx4kSKi4tdzpednc2UKVPo06cP3t7eeHt7M2jQIJYtW+Y0LicnB8MwAMjNza23viM5OdklFjt7Tc+VtSClpaUYhsHkyZPZs2cPcXFx+Pv7YxgGpaWljnGZmZncd999+Pr64uXlRXh4OAsXLsRqtV7Xa+jt7c2YMWPo2rVro4/5afICdYlNbGwsAAcOHLiuWNqytx4byC+7+zD9kyKG/GEL0z8p4pfdfViaMNDdoYmIiDgxmUwseXMxF0p2cnLtfC6Vf0ftpfNcKv+Ok2vnc6FkJ0sWL1IBv7jQFRggKSnJUVdhr48BHLUbJSUlREVFUV5ezv33309sbCyVlZWsWbOGzZs3s2XLFiIiflzh6fXXX+fAgQNERkYyYcIETp8+zaZNm3j22WfZu3cvixYtcpw/KSmJlJQUzGaz07Q2ewzXy/74ffv2ZdKkSVRXV9O+fd0yurNnz2bBggUEBwczceJEfHx82LZtG7NmzaKwsJDVq1f/Q0hrJRgAAA/hSURBVI/9j6itrWXTpk0ATjVK0jhdO7Xn46cjtA+MiIi0CnFxcaSnp9ftA/OXWY72HuYQlmgJZWmIrQ1Zvny5DbAtX768SX3Dhg2zeXp62r744gun9r1799o6d+5s69evn1P7999/73KOy5cv28aMGWMzmUy2srIypz7ANnLkyHpjTkpKsgG27OzsRsV88OBBG2ADbHPnznU55osvvrABtgcffNB27tw5R3ttba3tueeeswG29PT0emNpigceeMAG2A4ePHjNsUlJSbakpCTb//2//9f2i1/8wgbYJk+e3OD4q32vREREpPWxWCy27Oxs2yeffGLLzs62WSwWd4ckLZimkF1DUVERO3bsYNKkSYwZM8apr3fv3kydOpXdu3c7TSULDQ11OY+npyfPPfccVquV7OzsZo87MDCQOXPmuLS/8847ALz33nvcdtuPRd2GYfDaa69hGAZpaWnNHt+VUlJSSElJ4d/+7d/Yu3cvL730Eu+///5NjUFERETcx2QyERUVxWOPPUZUVJSmjclVaQrZNRQUFABw7NgxkpOTXfr37Nnj+Gqf8nT27FkWLlxIRkYGJSUlnDt3zukY+4IBzal///6OKWNXKigooFOnTnzwwQf1HtexY0fHc7pZbDYbtbW1HD16lPXr1/P73/+e/Px8Nm7ciI+Pz02NRURERG4+q9VKXl4eFRUVBAUFMXz4cCUx0iAlMNdgXwp4w4YNbNiwocFx9iSlpqaGqKgodu3axcCBAx17ynh6elJaWsqKFSu4dOlSs8fdrVu3eturq6uxWCykpKQ0eOxPE66bwcPDg+DgYJ577jn8/Px49NFH+cMf/sDrr79+02NplSr3QPX30PVnEOC6FKWIiEhLtXbtWn734guUHjriaAvpGcyiN5eqBkbqpQTmGuxXAN5++22mT59+zfGZmZns2rWLZ555xmUa1KeffsqKFSua9Pj2fVAsFotL35kzZxo8zr662U/5+PhgGAYnT55sUhw30/333w/Qqjf2vGlO7IP1M6Dsyx/bfhYFEz+ETn7uikpERKRR1q5dS3x8PA/18iTt6dsIDzBRXGnlj9uPEx8fT7oK+aUeqoG5BvvqYvn5+Y0aX1JSAsDDDz/s0peXl1fvMR4eHg0uX+zrW7erenl5uUtfUVFRo2K6UkREBFVVVezfv7/Jx94s9il2np7Krx1qztV/+/cIOP53eCQVfrev7uuxYkif7DxORESkhbFarfzuxRd4qJcnGQleRAZ74t3eIDLYk4xfe/FQb09emjnjurd4kFuXEphrGDJkCBEREaSlpfHZZ5+59NfW1pKbm+u4bzabAdi+fbvTuNzc3AYL07t27cqRI0fq7Rs0aBAAH330EbW1tY72/Px8Vq5c2bQnAzz//PMATJkyhaqqKpf+Y8eO8d133zX5vE21Z88eKisrXdrPnz/PzJkzAXjwwQebPY5W44/d67/ZaiFmCfSdAJ271X2N/lc4uM15nIiISAuTl5dH6aEjzB7eDo+fzBzxMAx+f287DpYdbvAPwNJ26U/cjZCWlsaoUaNISEhgyZIl3HPPPXh5eXHo0CHy8/M5ceIEFy9eBCAmJoaQkBDeeOMNiouLCQ8PZ+/evaxfv57Y2FjWrFnjcv7Ro0ezatUq4uPjGThwICaTiXHjxtGvXz8iIyMZOnQoW7duZejQoYwYMYKysjKysrKIiYlh3bp1TXouY8eOZe7cubz66quEhYUxduxYzGYzVVVVHDhwgLy8PObPn89dd93V5NfppZdeckxN2717t6PN29sbgJdffplf/KKuPmPTpk388z//M1FRUfzsZz/j9ttvp7y8nM8//5yqqiruvfdeRyIj19BzmPN9873uiUNERKQJKioqAAgPqL9Y395uHydipwSmEUJDQykqKmLx4sVkZGTw4YcfYjKZCAoKYsSIEcTHxzvGent7s3XrVmbNmsW2bdvIycmhb9++rFy5km7dutWbwCxduhSArVu3sm7dOmprawkMDKRfv34YhkFWVhYzZ85kw4YN7N69m/79+5OVlcXRo0ebnMAAzJs3jxEjRvDWW2+xZcsWTp8+jZ+fH6GhoSQnJ/P4449f1+uUnp5OWVmZU9uVz3fy5MmOBOb//J//w9NPP8327dvZuXMnZ8+e5fbbbyc8PJyEhASeeeYZTSG70ux6Vq47sQ/ej4JDO+quvNjZ62Gm5sAdvW9GdCIiIk0WFBQEQHGllchg18/84kqr0zgRO8Nms9ncHcTNkpqaSmJiIsuXL3fa9V5aF30fr7A8Gk7srZs2Zr63LnnZOAsC+8FTGe6OTkREpEFWq5Wwn4XQr+NxMn7t5TSNrNZm+//bu7eYqM41jOPPKqM0QUCMGoyxM1YlHhNsmgYSiUEbSXthJuIBTxW0pHfGpL0ArMEDtlXbbvZdoxMZI7Gp2SKJbS+MMYgYNL0RJR46GqCoTYixwGCjdWB6QRjDZgx2ysw68P/dmJlZMY/fyqzXd75vfUveH56p9VmmAvfb2FIZw4zLe2BKSkpkGIaKiorMjoJ/YMuWLTIMQyUlJWZHsY71Jweblf+VSN9kDf6ZuUQqjP6cHwAArCIpKUnf/Oe/+vHXkLw/PFNzZ0jB52E1dw6+/vHXkL7+tprmBSOMqxmY69evq76+PvJ68eLFw5Z/wdrq6up048aNyGuv16vs7GzzAlkJz4EBANhUtOfAzHbP0tffVrOFMqIaVw0MXl97e7v8fv+ox02ePFm7du2Kex4AAOBc/f39unz5sn7//XfNmDFDeXl5zLzglWhgEFVDQ4Py8/NHPc7tdqu9vT3+gQAAAADRwAAAAACwkXF5Ez8AAAAAe6KBAQAAAGAbNDAAAAAAbIMGBgAAAIBt0MAAAAAAsA2X2QGARAiHwwoGg2bHAABgmNTUVBmGYXaMuKD2IlajfS9oYDAuBINBpaenmx0DAIBhenp6lJaWZnaMuKD2IlajfS94DgzGhfH2K1Bvb69mzZqlzs5OxxbGeGHsYsfYxY6xi53dx44ZmJfsfi4xUqznlBkYQJJhGOPyYpiWljYu/91jgbGLHWMXO8Yudoyd9cRaezmXzjPW55Sb+AEAAADYBg0MAAAAANuggQEcKDk5WZWVlUpOTjY7iu0wdrFj7GLH2MWOsXMOzqXzxOucchM/AAAAANtgBgYAAACAbdDAAAAAALANGhgAAAAAtkEDAwAAAMA2aGAAB/nll1/04YcfKiMjQykpKXrvvfd06tQps2NZXm1trT755BO9++67Sk5OlmEY8vv9ZseyvIcPH6q6ulqrVq3SW2+9pYkTJyozM1OFhYW6du2a2fEsrbu7Wzt37lRubq4yMzOVnJysmTNnasWKFTpz5ozYX+f1HT58WIZhyDAMXb161ew4+Ae49jpPouoCu5ABDtHQ0KCCggJNnDhRRUVFSk9PV11dndra2nTw4EFVVFSYHdGyPB6POjo6NHXqVKWkpKijo0M1NTUqLi42O5qllZWV6dChQ5ozZ46WL1+u6dOnKxAIqL6+XuFwWN9//73Wr19vdkxLunfvnrKzs5WTk6O5c+dqypQp6urq0rlz59TV1aXS0lIdPXrU7JiWd/v2bS1dulQul0tPnz5Vc3OzcnJyzI6F18S113kSVRdoYAAHCIVCmj9/vh48eKDm5mYtXbpUkhQMBpWbm6u7d+/q1q1bmjdvnslJrenChQuaN2+e3G63vvrqK5WXl1NEX0NdXZ2mTZumvLy8Ye9fvnxZK1euVGpqqh49esQzHaLo7+9XOByWy+Ua9n4wGFROTo5u3bql1tZWLVq0yKSE1tff36/c3FwZhqGsrCzV1tbSwNgM117nSVRdYAkZ4AAXL17U/fv3tWnTpkjzIkmpqanas2ePQqGQampqTExobe+//77cbrfZMWxnzZo1I4qUJOXl5Sk/P19PnjzRzZs3TUhmfUlJSSOaF2nwO1tQUCBpcJYGr3bo0CG1tLTo+PHjSkpKMjsOYsC113kSVRdoYAAHaGhokCStWrVqxGdD7126dCmRkTDOTZgwQZKi/icdr/bs2TNdvHhRhmFo4cKFZsexrNbWVu3bt0+ff/45s1SATYxlXaCyAA4QCAQkKeoSsYyMDE2dOjVyDBBvv/32my5cuKDMzEwtWbLE7DiW1t3drerqag0MDKirq0s///yzOjs7VVlZyZLPVwiFQiouLtaCBQtUVlZmdhwAr2Gs6wINDOAAPT09kqT09PSon6elpenBgweJjIRx6sWLF9q6daueP3+uw4cPs7RnFN3d3dq3b1/k9YQJE3TkyBF9+umnJqayti+++EItLS26du1a5BddANYVj7rAEjIAwJgYGBjQ9u3b1djYqNLSUm3dutXsSJbn8XgUDocVCoXU1tam/fv3a/fu3SosLFQoFDI7nuW0tLSoqqpKn332md555x2z4wAYRbzqAg0M4ABDMy9DMzH/r7e395WzM8BYCIfDKi0tVW1trbZs2aLvvvvO7Ei2kpSUJI/Ho7KyMlVVVens2bM6duyY2bEsZ9u2bZozZ4727t1rdhQAo4hnXaCBARxgaK18tPtc/vjjDz1+/Jj19IibgYEB7dixQ8ePH9fGjRvl9/v1xhuUl1gNbbwxtDkHXmppadGdO3f05ptvRh5eaRiGTpw4IUmRbZXr6+vNDQqMc/GuC9wDAzjA8uXL9eWXX+r8+fMqKioa9tn58+cjxwBjbWBgQB9//LFqamq0YcMGnTx5kvte/qVHjx5JYge3aHbs2BH1/cbGRgUCAa1evVrTpk2Tx+NJbDAAEYmoC1wdAQdYuXKl3n77bZ06dUo7d+5Udna2pMGH4h04cEAul4sHg2HMDf3C5vf7tW7dOtXW1tK8vKbr169r9uzZI5Z2PnnyRBUVFZKkDz74wIxolubz+aK+X1xcrEAgoPLych5kCZgoUXWBBgZwAJfLJZ/Pp4KCAuXl5Wnjxo1KS0tTXV2d2traVFVVpaysLLNjWpbP51NTU5MkRR6w5fP5Ikt4vF6vvF6vSemsa//+/fL7/Zo0aZKysrJUVVU14hiv1xtpqPGS3++Xz+dTfn6+3G63UlJS1NHRoZ9++kl9fX0qLCzUpk2bzI4JxBXXXudJVF2ggQEcIj8/X01NTaqsrNTp06f1119/adGiRTpw4IA2b95sdjxLa2pqiqyhH3LlyhVduXJF0uBOURTRkdrb2yVJfX19OnjwYNRjPB4PDUwUa9euVU9Pj65evarGxkb9+eefmjJlipYtW6aPPvpIRUVFMgzD7JhAXHHtdZ5E1QUjHA6H/9XfAAAAAAAJwjYxAAAAAGyDBgYAAACAbdDAAAAAALANGhgAAAAAtkEDAwAAAMA2aGAAAAAA2AYNDAAAAADboIEBAAAAYBs0MAAAAABsgwYGAAAAgG3QwAAAAACwDRoYAAAAALZBAwMAAADANv4GLYBBhskKmWwAAAAASUVORK5CYII=",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {},
@@ -786,6 +1431,7 @@
     "    var_names=[\"beta_z\"],\n",
     "    combined=True,\n",
     "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    "    r_hat=True,\n",
     ")\n",
     "axs[0].set_title(\"Parameter Comparison Outcome Model \\n Baseline v Spike and Slab\");"
    ]
@@ -800,15 +1446,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "id": "acafc928",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "
"
+       "
"
       ]
      },
      "metadata": {},
@@ -821,6 +1467,7 @@
     "    var_names=[\"beta_t\"],\n",
     "    combined=True,\n",
     "    model_names=[\"Spike and Slab\", \"Normal\"],\n",
+    "    r_hat=True,\n",
     ")\n",
     "\n",
     "axs[0].set_title(\"Parameter Comparison Treatment Model \\n Baseline v Spike and Slab\");"
@@ -828,7 +1475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "f2a0b213",
    "metadata": {},
    "outputs": [
@@ -861,125 +1508,125 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      0.0105\n",
+       "      0.01750\n",
        "      False\n",
-       "      0.014042\n",
+       "      0.020546\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      1.0000\n",
+       "      1.00000\n",
        "      True\n",
-       "      0.990424\n",
+       "      0.991285\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.7605\n",
+       "      0.64100\n",
        "      True\n",
-       "      0.775804\n",
+       "      0.659357\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      0.6880\n",
+       "      0.58200\n",
        "      True\n",
-       "      0.719690\n",
+       "      0.616580\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      0.0570\n",
+       "      0.17625\n",
        "      False\n",
-       "      0.064343\n",
+       "      0.192949\n",
        "    \n",
        "    \n",
        "      5\n",
-       "      0.0330\n",
+       "      0.06075\n",
        "      False\n",
-       "      0.038359\n",
+       "      0.068297\n",
        "    \n",
        "    \n",
        "      6\n",
-       "      0.6755\n",
+       "      0.66600\n",
        "      True\n",
-       "      0.704165\n",
+       "      0.702671\n",
        "    \n",
        "    \n",
        "      7\n",
-       "      0.0085\n",
+       "      0.01000\n",
        "      False\n",
-       "      0.011460\n",
+       "      0.012679\n",
        "    \n",
        "    \n",
        "      8\n",
-       "      0.0170\n",
+       "      0.01300\n",
        "      False\n",
-       "      0.021624\n",
+       "      0.016342\n",
        "    \n",
        "    \n",
        "      9\n",
-       "      0.0130\n",
+       "      0.00975\n",
        "      False\n",
-       "      0.015590\n",
+       "      0.012392\n",
        "    \n",
        "    \n",
        "      10\n",
-       "      0.0080\n",
+       "      0.01700\n",
        "      False\n",
-       "      0.010675\n",
+       "      0.019508\n",
        "    \n",
        "    \n",
        "      11\n",
-       "      0.0250\n",
+       "      0.01625\n",
        "      False\n",
-       "      0.029619\n",
+       "      0.021217\n",
        "    \n",
        "    \n",
        "      12\n",
-       "      0.0060\n",
+       "      0.02000\n",
        "      False\n",
-       "      0.008689\n",
+       "      0.024469\n",
        "    \n",
        "    \n",
        "      13\n",
-       "      0.0250\n",
+       "      0.01825\n",
        "      False\n",
-       "      0.028057\n",
+       "      0.023474\n",
        "    \n",
        "    \n",
        "      14\n",
-       "      0.0135\n",
+       "      0.02700\n",
        "      False\n",
-       "      0.016080\n",
+       "      0.031636\n",
        "    \n",
        "    \n",
        "      15\n",
-       "      0.0135\n",
+       "      0.01650\n",
        "      False\n",
-       "      0.015825\n",
+       "      0.020863\n",
        "    \n",
        "  \n",
        "\n",
        ""
       ],
       "text/plain": [
-       "      prob  selected  gamma_mean\n",
-       "0   0.0105     False    0.014042\n",
-       "1   1.0000      True    0.990424\n",
-       "2   0.7605      True    0.775804\n",
-       "3   0.6880      True    0.719690\n",
-       "4   0.0570     False    0.064343\n",
-       "5   0.0330     False    0.038359\n",
-       "6   0.6755      True    0.704165\n",
-       "7   0.0085     False    0.011460\n",
-       "8   0.0170     False    0.021624\n",
-       "9   0.0130     False    0.015590\n",
-       "10  0.0080     False    0.010675\n",
-       "11  0.0250     False    0.029619\n",
-       "12  0.0060     False    0.008689\n",
-       "13  0.0250     False    0.028057\n",
-       "14  0.0135     False    0.016080\n",
-       "15  0.0135     False    0.015825"
+       "       prob  selected  gamma_mean\n",
+       "0   0.01750     False    0.020546\n",
+       "1   1.00000      True    0.991285\n",
+       "2   0.64100      True    0.659357\n",
+       "3   0.58200      True    0.616580\n",
+       "4   0.17625     False    0.192949\n",
+       "5   0.06075     False    0.068297\n",
+       "6   0.66600      True    0.702671\n",
+       "7   0.01000     False    0.012679\n",
+       "8   0.01300     False    0.016342\n",
+       "9   0.00975     False    0.012392\n",
+       "10  0.01700     False    0.019508\n",
+       "11  0.01625     False    0.021217\n",
+       "12  0.02000     False    0.024469\n",
+       "13  0.01825     False    0.023474\n",
+       "14  0.02700     False    0.031636\n",
+       "15  0.01650     False    0.020863"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1001,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 9,
    "id": "63edfa4e",
    "metadata": {},
    "outputs": [
@@ -1027,16 +1674,18 @@
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/causalpy/pymc_models.py:699: UserWarning: Variable selection priors specified. The 'mus' and 'sigmas' in the priors dict will be ignored for beta coefficients. Only 'eta' and 'lkj_sd' will be used.\n",
       "  warnings.warn(\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
       "NUTS: [tau_beta_t, lambda_beta_t, c2_beta_t, beta_t_raw, tau_beta_z, lambda_beta_z, c2_beta_z, beta_z_raw, chol_cov]\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
       "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1407969a4c4946d0920f0defc2ec153c",
+       "model_id": "d795e2e885b74a708646d93e4d96f152",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1051,6 +1700,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
+      "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
+      "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
       "  outputs = vm() if output_subset is None else vm(output_subset=output_subset)\n",
       "/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/pytensor/compile/function/types.py:1039: RuntimeWarning: invalid value encountered in accumulate\n",
@@ -1071,10 +1724,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 2_000 tune and 1_000 draw iterations (4_000 + 2_000 draws total) took 743 seconds.\n",
-      "There was 1 divergence after tuning. Increase `target_accept` or reparameterize.\n",
-      "We recommend running at least 4 chains for robust computation of convergence diagnostics\n",
-      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 644 seconds.\n",
+      "There were 11 divergences after tuning. Increase `target_accept` or reparameterize.\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     }
@@ -1099,10 +1750,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 10,
    "id": "9c283ee1",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[autoreload of cutils_ext failed: Traceback (most recent call last):\n",
+      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/IPython/extensions/autoreload.py\", line 325, in check\n",
+      "    superreload(m, reload, self.old_objects)\n",
+      "    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/IPython/extensions/autoreload.py\", line 580, in superreload\n",
+      "    module = reload(module)\n",
+      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/importlib/__init__.py\", line 128, in reload\n",
+      "    raise ModuleNotFoundError(f\"spec not found for the module {name!r}\", name=name)\n",
+      "ModuleNotFoundError: spec not found for the module 'cutils_ext'\n",
+      "]\n"
+     ]
+    },
     {
      "data": {
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        "    \n",
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        "      1\n",
-       "      1.574053\n",
-       "      75.808614\n",
-       "      0.031037\n",
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        "    \n",
        "    \n",
        "      2\n",
-       "      0.454083\n",
-       "      21.869287\n",
-       "      0.031037\n",
+       "      0.164625\n",
+       "      12.062918\n",
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        "    \n",
        "    \n",
        "      3\n",
-       "      0.416588\n",
-       "      20.063451\n",
-       "      0.031037\n",
+       "      0.136282\n",
+       "      9.986066\n",
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        "    \n",
        "    \n",
        "      4\n",
-       "      0.078152\n",
-       "      3.763922\n",
-       "      0.031037\n",
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+       "      6.658563\n",
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        "    \n",
        "    \n",
        "      5\n",
-       "      0.043892\n",
-       "      2.113912\n",
-       "      0.031037\n",
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+       "      2.216801\n",
+       "      0.038199\n",
        "    \n",
        "    \n",
        "      6\n",
-       "      0.388261\n",
-       "      18.699190\n",
-       "      0.031037\n",
+       "      0.199524\n",
+       "      14.620131\n",
+       "      0.038199\n",
        "    \n",
        "    \n",
        "      7\n",
-       "      0.027243\n",
-       "      1.312060\n",
-       "      0.031037\n",
+       "      0.015415\n",
+       "      1.129522\n",
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        "    \n",
        "    \n",
        "      8\n",
-       "      0.027663\n",
-       "      1.332293\n",
-       "      0.031037\n",
+       "      0.015330\n",
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        "    \n",
        "    \n",
        "      9\n",
-       "      0.026640\n",
-       "      1.283037\n",
-       "      0.031037\n",
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        "      10\n",
-       "      0.026496\n",
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-       "      0.031037\n",
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        "    \n",
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        "      11\n",
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-       "      0.031037\n",
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        "    \n",
        "    \n",
        "      12\n",
-       "      0.024832\n",
-       "      1.195942\n",
-       "      0.031037\n",
+       "      0.021961\n",
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        "    \n",
        "    \n",
        "      13\n",
-       "      0.032776\n",
-       "      1.578531\n",
-       "      0.031037\n",
+       "      0.020631\n",
+       "      1.511758\n",
+       "      0.038199\n",
        "    \n",
        "    \n",
        "      14\n",
-       "      0.026933\n",
-       "      1.297110\n",
-       "      0.031037\n",
+       "      0.021207\n",
+       "      1.553953\n",
+       "      0.038199\n",
        "    \n",
        "    \n",
        "      15\n",
-       "      0.024878\n",
-       "      1.198152\n",
-       "      0.031037\n",
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        "    \n",
        "  \n",
        "\n",
@@ -1232,25 +1899,25 @@
       ],
       "text/plain": [
        "    shrinkage_factor  lambda_tilde       tau\n",
-       "0           0.024168      1.163944  0.031037\n",
-       "1           1.574053     75.808614  0.031037\n",
-       "2           0.454083     21.869287  0.031037\n",
-       "3           0.416588     20.063451  0.031037\n",
-       "4           0.078152      3.763922  0.031037\n",
-       "5           0.043892      2.113912  0.031037\n",
-       "6           0.388261     18.699190  0.031037\n",
-       "7           0.027243      1.312060  0.031037\n",
-       "8           0.027663      1.332293  0.031037\n",
-       "9           0.026640      1.283037  0.031037\n",
-       "10          0.026496      1.276087  0.031037\n",
-       "11          0.031909      1.536788  0.031037\n",
-       "12          0.024832      1.195942  0.031037\n",
-       "13          0.032776      1.578531  0.031037\n",
-       "14          0.026933      1.297110  0.031037\n",
-       "15          0.024878      1.198152  0.031037"
+       "0           0.016681      1.222294  0.038199\n",
+       "1           0.847573     62.105904  0.038199\n",
+       "2           0.164625     12.062918  0.038199\n",
+       "3           0.136282      9.986066  0.038199\n",
+       "4           0.090871      6.658563  0.038199\n",
+       "5           0.030253      2.216801  0.038199\n",
+       "6           0.199524     14.620131  0.038199\n",
+       "7           0.015415      1.129522  0.038199\n",
+       "8           0.015330      1.123328  0.038199\n",
+       "9           0.015992      1.171778  0.038199\n",
+       "10          0.015676      1.148635  0.038199\n",
+       "11          0.020536      1.504805  0.038199\n",
+       "12          0.021961      1.609202  0.038199\n",
+       "13          0.020631      1.511758  0.038199\n",
+       "14          0.021207      1.553953  0.038199\n",
+       "15          0.019708      1.444109  0.038199"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1264,23 +1931,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 14,
    "id": "82b0121c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       ""
+       ""
       ]
      },
-     "execution_count": 134,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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",
       "text/plain": [
        "
"
       ]

From 896ee5760dae71d9827501275ad36e8621130553 Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 21:25:09 +0000
Subject: [PATCH 12/17] update index

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/index.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/source/notebooks/index.md b/docs/source/notebooks/index.md
index c9ae0ad7..be879e33 100644
--- a/docs/source/notebooks/index.md
+++ b/docs/source/notebooks/index.md
@@ -65,6 +65,7 @@ rkink_pymc.ipynb
 
 iv_pymc.ipynb
 iv_weak_instruments.ipynb
+iv_vs_priors.ipynb
 :::
 
 :::{toctree}

From 068c92242cd528f0a6e2df413b40e8b4c4c1cd2b Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Fri, 21 Nov 2025 21:49:08 +0000
Subject: [PATCH 13/17] update spelling

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index bd12eed6..3d4af100 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -525,7 +525,7 @@
     "So we have the \"spike\"—the coefficient is exactly zero. When $\\gamma_{j} = 1$, we have the \"slab\" i.e. the coefficient takes on a value from the raw distribution.\n",
     "This approach appeals to our intuition about many real-world scenarios. Consider a propensity score model predicting whether someone receives a treatment. Some demographic variables might genuinely have no relationship with treatment assignment, while others are strongly predictive. The spike-and-slab says: let's let each variable clearly declare itself as relevant or irrelevant.\n",
     "\n",
-    "### The Regularised Horseshoe: Continuos Moderation\n",
+    "### The Regularised Horseshoe: Gentle Moderation\n",
     "\n",
     "The horseshoe prior takes a different philosophical stance. Instead of discrete selection, it says: effects exist on a continuum from negligible to substantial, and we should shrink them proportionally to their signal strength. Small effects get heavily shrunk (possibly to near-zero), while large effects escape shrinkage almost entirely.\n",
     "\n",

From 13b320e6652c39ff71a6853fa72b25b62668609d Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Sat, 22 Nov 2025 22:00:25 +0000
Subject: [PATCH 14/17] add binary treatment case to IV model

Signed-off-by: Nathaniel 
---
 causalpy/experiments/instrumental_variable.py |  34 ++++--
 causalpy/pymc_models.py                       | 105 +++++++++++++-----
 .../tests/test_integration_pymc_examples.py   |  31 ++++++
 docs/source/_static/interrogate_badge.svg     |   6 +-
 4 files changed, 136 insertions(+), 40 deletions(-)

diff --git a/causalpy/experiments/instrumental_variable.py b/causalpy/experiments/instrumental_variable.py
index 5b6b4128..4fdf7429 100644
--- a/causalpy/experiments/instrumental_variable.py
+++ b/causalpy/experiments/instrumental_variable.py
@@ -57,6 +57,10 @@ class InstrumentalVariable(BaseExperiment):
     vs_hyperparams : dict, optional
         Hyperparameters for variable selection priors. Only used if vs_prior_type
         is not None.
+    binary_treatment : bool, default=False
+        A indicator for whether the treatment to be modelled is binary or not.
+        Determines which PyMC model we use to model the joint outcome and
+        treatment.
 
     Example
     --------
@@ -116,6 +120,7 @@ def __init__(
         priors: dict | None = None,
         vs_prior_type=None,
         vs_hyperparams=None,
+        binary_treatment=False,
         **kwargs: dict,
     ) -> None:
         super().__init__(model=model)
@@ -127,6 +132,7 @@ def __init__(
         self.model = model
         self.vs_prior_type = vs_prior_type
         self.vs_hyperparams = vs_hyperparams or {}
+        self.binary_treatment = binary_treatment
         self.input_validation()
 
         y, X = dmatrices(formula, self.data)
@@ -150,12 +156,22 @@ def __init__(
         COORDS = {"instruments": self.labels_instruments, "covariates": self.labels}
         self.coords = COORDS
         if priors is None:
-            priors = {
-                "mus": [self.ols_beta_first_params, self.ols_beta_second_params],
-                "sigmas": [10, 10],
-                "eta": 2,
-                "lkj_sd": 1,
-            }
+            if binary_treatment:
+                # Different default priors for binary treatment
+                priors = {
+                    "mus": [self.ols_beta_first_params, self.ols_beta_second_params],
+                    "sigmas": [1, 1],
+                    "sigma_U": 1.0,
+                    "rho_bounds": [-0.99, 0.99],
+                }
+            else:
+                # Original continuous treatment priors
+                priors = {
+                    "mus": [self.ols_beta_first_params, self.ols_beta_second_params],
+                    "sigmas": [1, 1],
+                    "eta": 2,
+                    "lkj_sd": 1,
+                }
         self.priors = priors
         self.model.fit(  # type: ignore[call-arg,union-attr]
             X=self.X,
@@ -166,6 +182,7 @@ def __init__(
             priors=self.priors,
             vs_prior_type=vs_prior_type,
             vs_hyperparams=vs_hyperparams,
+            binary_treatment=self.binary_treatment,
         )
 
     def input_validation(self) -> None:
@@ -186,9 +203,8 @@ def input_validation(self) -> None:
         if check_binary:
             warnings.warn(
                 """Warning. The treatment variable is not Binary.
-                This is not necessarily a problem but it violates
-                the assumption of a simple IV experiment.
-                The coefficients should be interpreted appropriately."""
+                We will use the multivariate normal likelihood
+                for continuous treatment."""
             )
 
     def get_2SLS_fit(self) -> None:
diff --git a/causalpy/pymc_models.py b/causalpy/pymc_models.py
index 1c0b0a0f..71f5210e 100644
--- a/causalpy/pymc_models.py
+++ b/causalpy/pymc_models.py
@@ -683,6 +683,7 @@ def build_model(  # type: ignore
         priors,
         vs_prior_type=None,
         vs_hyperparams=None,
+        binary_treatment=False,
     ) -> None:
         """Specify model with treatment regression and focal regression
         data and priors.
@@ -709,6 +710,8 @@ def build_model(  # type: ignore
                               or "horseshoe" or "normal
         vs_hyperparams: An optional dictionary of priors for the
                                variable selection hyperparameters
+        binary_treatment: A flag for determining the relevant
+                                likelihood to be used.
 
         """
 
@@ -755,31 +758,74 @@ def build_model(  # type: ignore
                     dims="covariates",
                 )
 
-            sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2)
-            chol, _, _ = pm.LKJCholeskyCov(
-                name="chol_cov",
-                eta=priors["eta"],
-                n=2,
-                sd_dist=sd_dist,
-            )
-            # compute and store the covariance matrix
-            pm.Deterministic(name="cov", var=pt.dot(l=chol, r=chol.T))
-
-            # --- Parameterization ---
-            mu_y = pm.Deterministic(name="mu_y", var=pt.dot(X, beta_z))
-            # focal regression
-            mu_t = pm.Deterministic(name="mu_t", var=pt.dot(Z, beta_t))
-            # instrumental regression
-            mu = pm.Deterministic(name="mu", var=pt.stack(tensors=(mu_y, mu_t), axis=1))
-
-            # --- Likelihood ---
-            pm.MvNormal(
-                name="likelihood",
-                mu=mu,
-                chol=chol,
-                observed=np.stack(arrays=(y.flatten(), t.flatten()), axis=1),
-                shape=(X.shape[0], 2),
-            )
+            if binary_treatment:
+                # Binary treatment formulation with correlated latent errors
+                sigma_U = pm.Exponential("sigma_U", priors.get("sigma_U", 1.0))
+
+                # Correlation parameter with bounds
+                rho_lower = priors.get("rho_bounds", [-0.99, 0.99])[0]
+                rho_upper = priors.get("rho_bounds", [-0.99, 0.99])[1]
+
+                # Use tanh transform to keep correlation in valid range
+                rho_unconstr = pm.Normal("rho_unconstr", 0, 0.5)
+                rho = pm.Deterministic("rho", pm.math.tanh(rho_unconstr))
+
+                # Clip to ensure numerical stability
+                rho_clipped = pt.clip(rho, rho_lower + 0.01, rho_upper - 0.01)
+
+                # Cholesky decomposition for correlated errors
+                inverse_rho = pm.math.sqrt(pm.math.maximum(1 - rho_clipped**2, 1e-12))
+                chol = pt.stack([[sigma_U, 0.0], [sigma_U * rho_clipped, inverse_rho]])
+
+                # Draw latent errors
+                eps_raw = pm.Normal("eps_raw", 0, 1, shape=(X.shape[0], 2))
+                eps = pm.Deterministic("eps", pt.dot(eps_raw, chol.T))
+
+                U = eps[:, 0]  # Outcome error
+                V = eps[:, 1]  # Treatment error
+
+                # Treatment equation (logit link for binary treatment)
+                mu_treatment = pm.Deterministic("mu_t", pt.dot(Z, beta_t) + V)
+                p_t = pm.math.invlogit(mu_treatment)
+                pm.Bernoulli("likelihood_treatment", p=p_t, observed=t.flatten())
+
+                # Outcome equation
+                mu_outcome = pm.Deterministic("mu_y", pt.dot(X, beta_z) + U)
+                pm.Normal(
+                    "likelihood_outcome",
+                    mu=mu_outcome,
+                    sigma=sigma_U,
+                    observed=y.flatten(),
+                )
+
+            else:
+                sd_dist = pm.Exponential.dist(priors["lkj_sd"], shape=2)
+                chol, _, _ = pm.LKJCholeskyCov(
+                    name="chol_cov",
+                    eta=priors["eta"],
+                    n=2,
+                    sd_dist=sd_dist,
+                )
+                # compute and store the covariance matrix
+                pm.Deterministic(name="cov", var=pt.dot(l=chol, r=chol.T))
+
+                # --- Parameterization ---
+                mu_y = pm.Deterministic(name="mu_y", var=pt.dot(X, beta_z))
+                # focal regression
+                mu_t = pm.Deterministic(name="mu_t", var=pt.dot(Z, beta_t))
+                # instrumental regression
+                mu = pm.Deterministic(
+                    name="mu", var=pt.stack(tensors=(mu_y, mu_t), axis=1)
+                )
+
+                # --- Likelihood ---
+                pm.MvNormal(
+                    name="likelihood",
+                    mu=mu,
+                    chol=chol,
+                    observed=np.stack(arrays=(y.flatten(), t.flatten()), axis=1),
+                    shape=(X.shape[0], 2),
+                )
 
     def sample_predictive_distribution(self, ppc_sampler: str | None = "jax") -> None:
         """Function to sample the Multivariate Normal posterior predictive
@@ -813,7 +859,7 @@ def sample_predictive_distribution(self, ppc_sampler: str | None = "jax") -> Non
                         )
                     )
 
-    def fit(
+    def fit(  # type: ignore[override]
         self,
         X,
         Z,
@@ -824,7 +870,8 @@ def fit(
         ppc_sampler=None,
         vs_prior_type=None,
         vs_hyperparams=None,
-    ):
+        binary_treatment: bool = False,
+    ):  # type: ignore[override]
         """Draw samples from posterior distribution and potentially
         from the prior and posterior predictive distributions. The
         fit call can take values for the
@@ -838,7 +885,9 @@ def fit(
         # sample_posterior_predictive() if provided in sample_kwargs.
         # Use JAX for ppc sampling of multivariate likelihood
 
-        self.build_model(X, Z, y, t, coords, priors, vs_prior_type, vs_hyperparams)
+        self.build_model(
+            X, Z, y, t, coords, priors, vs_prior_type, vs_hyperparams, binary_treatment
+        )
         with self:
             self.idata = pm.sample(**self.sample_kwargs)
         self.sample_predictive_distribution(ppc_sampler=ppc_sampler)
diff --git a/causalpy/tests/test_integration_pymc_examples.py b/causalpy/tests/test_integration_pymc_examples.py
index 585c0b1f..6675a298 100644
--- a/causalpy/tests/test_integration_pymc_examples.py
+++ b/causalpy/tests/test_integration_pymc_examples.py
@@ -682,6 +682,37 @@ def test_iv_reg(mock_pymc_sample):
         result.get_plot_data()
 
 
+@pytest.mark.integration
+def test_iv_binary_treatment(mock_pymc_sample):
+    df = cp.load_data("risk")
+    df["binary_trt"] = np.random.binomial(1, 0.5, len(df))
+    instruments_formula = "binary_trt  ~ 1 + risk + logmort0"
+    formula = "loggdp ~  1 + binary_trt + risk"
+    instruments_data = df[["risk", "logmort0", "binary_trt"]]
+    data = df[["loggdp", "risk", "binary_trt"]]
+
+    result = cp.InstrumentalVariable(
+        instruments_data=instruments_data,
+        data=data,
+        instruments_formula=instruments_formula,
+        formula=formula,
+        model=cp.pymc_models.InstrumentalVariableRegression(
+            sample_kwargs=sample_kwargs
+        ),
+        binary_treatment=True,
+    )
+    result.model.sample_predictive_distribution(ppc_sampler="pymc")
+    assert isinstance(df, pd.DataFrame)
+    assert isinstance(data, pd.DataFrame)
+    assert isinstance(instruments_data, pd.DataFrame)
+    assert isinstance(result, cp.InstrumentalVariable)
+    assert len(result.idata.posterior.coords["chain"]) == sample_kwargs["chains"]
+    assert len(result.idata.posterior.coords["draw"]) == sample_kwargs["draws"]
+    with pytest.raises(NotImplementedError):
+        result.get_plot_data()
+    assert "rho" in result.model.named_vars
+
+
 @pytest.mark.integration
 def test_iv_reg_vs_prior(mock_pymc_sample):
     df = cp.load_data("risk")
diff --git a/docs/source/_static/interrogate_badge.svg b/docs/source/_static/interrogate_badge.svg
index 5fef2054..4ed4f3af 100644
--- a/docs/source/_static/interrogate_badge.svg
+++ b/docs/source/_static/interrogate_badge.svg
@@ -1,5 +1,5 @@
 
-    interrogate: 94.8%
+    interrogate: 94.6%
     
         
             
@@ -12,8 +12,8 @@
     
         interrogate
         interrogate
-        94.8%
-        94.8%
+        94.6%
+        94.6%
     
     
         

From 62101166348b37ee28d8c88eae143e1975fdd33f Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Wed, 26 Nov 2025 21:34:30 +0000
Subject: [PATCH 15/17] adding more write up

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 426 +++++++++++++++++------
 1 file changed, 318 insertions(+), 108 deletions(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 3d4af100..3e293ad5 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -512,7 +512,7 @@
    "source": [
     "CausalPy's  `Variable Selection` module provides a way to encode our uncertainty about variable relevance directly into the prior distribution. Rather than choosing which predictors to include, we specify priors that allow coefficients to be shrunk toward zero (or exactly zero) when the data doesn't support their inclusion. The key insight is that variable selection becomes part of the inference problem rather than a preprocessing step. The module offers two fundamentally different approaches to variable selection, each reflecting a different belief about how sparsity manifests in the world.\n",
     "\n",
-    "### The Spike-and-Slab: Discrete Choices\n",
+    "#### The Spike-and-Slab: Discrete Choices\n",
     "\n",
     "The spike-and-slab prior embodies a binary worldview: each variable either matters or it doesn't. Mathematically, we express this as:\n",
     "\n",
@@ -525,13 +525,13 @@
     "So we have the \"spike\"—the coefficient is exactly zero. When $\\gamma_{j} = 1$, we have the \"slab\" i.e. the coefficient takes on a value from the raw distribution.\n",
     "This approach appeals to our intuition about many real-world scenarios. Consider a propensity score model predicting whether someone receives a treatment. Some demographic variables might genuinely have no relationship with treatment assignment, while others are strongly predictive. The spike-and-slab says: let's let each variable clearly declare itself as relevant or irrelevant.\n",
     "\n",
-    "### The Regularised Horseshoe: Gentle Moderation\n",
+    "#### The Regularised Horseshoe: Gentle Moderation\n",
     "\n",
     "The horseshoe prior takes a different philosophical stance. Instead of discrete selection, it says: effects exist on a continuum from negligible to substantial, and we should shrink them proportionally to their signal strength. Small effects get heavily shrunk (possibly to near-zero), while large effects escape shrinkage almost entirely.\n",
     "\n",
-    "$$ \\beta_{j} = \\tau \\cdot \\lambda_{j} \\cdot \\beta_{j\\text{raw}}$$\n",
+    "$$ \\beta_{j} = \\tau \\cdot \\tilde{\\lambda}_j \\cdot \\beta_{j\\text{raw}}$$\n",
     "\n",
-    "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\lambda_{j}$ is local or specific to each coefficient and regularised so as to ensure finite variance. \n"
+    "where $\\tau$ is a global shrinkage parameter shared across all coefficients, and $\\tilde{\\lambda}_j$ is local or specific to each coefficient and regularised so as to ensure finite variance. \n"
    ]
   },
   {
@@ -550,13 +550,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 52,
    "id": "ae848fe9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -570,7 +570,8 @@
     "axs = axs.flatten()\n",
     "axs[0].hist(pm.draw(pm.Beta.dist(2, 2), 1000), ec=\"black\", color=\"slateblue\")\n",
     "axs[1].hist(pm.draw(pm.Beta.dist(2, 5), 1000), ec=\"black\", color=\"slateblue\")\n",
-    "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\");"
+    "axs[2].hist(pm.draw(pm.Beta.dist(5, 2), 1000), ec=\"black\", color=\"slateblue\")\n",
+    "axs[1].set_title(r\"Various Distributions for the $\\pi$ hyperparameter\", size=20);"
    ]
   },
   {
@@ -583,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "763ca253",
    "metadata": {},
    "outputs": [
@@ -782,11 +783,7 @@
     "    formula=formula,\n",
     "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
     "    vs_prior_type=\"spike_and_slab\",\n",
-    "    vs_hyperparams={\n",
-    "        \"pi_alpha\": 2,\n",
-    "        \"pi_beta\": 2,\n",
-    "        \"slab_sigma\": 2,\n",
-    "    },\n",
+    "    vs_hyperparams={\"pi_alpha\": 2, \"pi_beta\": 2, \"slab_sigma\": 2, \"temperature\": 0.1},\n",
     ")"
    ]
   },
@@ -1473,6 +1470,14 @@
     "axs[0].set_title(\"Parameter Comparison Treatment Model \\n Baseline v Spike and Slab\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "07f7d95b",
+   "metadata": {},
+   "source": [
+    "The spike and slab prior can also output direct inclusion probabilities that can be used for communication regarding which variables were \"selected\" in the process."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -1643,12 +1648,63 @@
    "id": "38568d27",
    "metadata": {},
    "source": [
-    "## Horseshoe"
+    "### Horseshoe\n",
+    "\n",
+    "The horseshoe prior is a sophisticated continuous shrinkage method designed for regularization and variable selection in high-dimensional regression settings. Unlike discrete selection approaches, it operates through a elegant hierarchical structure that adaptively shrinks coefficients based on the strength of their signal in the data. The key to the implementation is the hierarchical $\\lambda$ component: \n",
+    "\n",
+    "$$ \\tilde{\\lambda}_j = \\sqrt{\\frac{c^2 \\lambda_j^2}{c^2 + \\tau^2 \\lambda_j^2}} $$\n",
+    "\n",
+    "is composed of individual local shrinkage parameters and $c^2$ is a regularization parameter that prevents over-shrinkage of genuinely large signals. \n",
+    "\n",
+    "#### The $\\tau_0$ hyperparameter\n",
+    "\n",
+    "Like the `temperature` parameter in the spike and slab model, the $\\tau_0$ parameter determines the overall level of sparsity expected in the model. However, the $tau_0$ will by default be derived from the data and the number of covariates in your data. While both the horseshoe and spike-and-slab priors address variable selection and sparsity, they embody fundamentally different philosophies about how to achieve these goals. The horseshoe embraces continuity, creating a smooth gradient of shrinkage where all coefficients remain in the model but are pulled toward zero with varying intensity. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 51,
+   "id": "16bb5f90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n",
+    "axs = axs.flatten()\n",
+    "axs[0].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(2, 2), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[1].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(3, 3), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[2].hist(\n",
+    "    pm.draw(pm.InverseGamma.dist(4, 4), 1000) ** 2,\n",
+    "    ec=\"black\",\n",
+    "    color=\"slateblue\",\n",
+    "    bins=30,\n",
+    ")\n",
+    "axs[1].set_title(r\"Various Distributions for the $c^{2}$ hyperparameter\", size=20);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "id": "63edfa4e",
    "metadata": {},
    "outputs": [
@@ -1685,7 +1741,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d795e2e885b74a708646d93e4d96f152",
+       "model_id": "1410b52c995348e397aed1a2843f2bb7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1724,8 +1780,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 644 seconds.\n",
-      "There were 11 divergences after tuning. Increase `target_accept` or reparameterize.\n",
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 534 seconds.\n",
+      "There were 16 divergences after tuning. Increase `target_accept` or reparameterize.\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
      ]
     }
@@ -1745,31 +1801,24 @@
     "    formula=formula,\n",
     "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
     "    vs_prior_type=\"horseshoe\",\n",
+    "    vs_hyperparams={\"c2_alpha\": 3, \"c2_beta\": 3},\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "db7d86aa",
+   "metadata": {},
+   "source": [
+    "Similar to the inclusion probabilities in the spike and slab model, a horseshoe model can output the relative shrinkage factor that gets applied to each variables inclusion. This method of variable is less decisive than spike and slab, but also mitigates case of completely zero-ing the small but real contributions of certain variables."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 41,
    "id": "9c283ee1",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[autoreload of cutils_ext failed: Traceback (most recent call last):\n",
-      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/IPython/extensions/autoreload.py\", line 325, in check\n",
-      "    superreload(m, reload, self.old_objects)\n",
-      "    ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
-      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/site-packages/IPython/extensions/autoreload.py\", line 580, in superreload\n",
-      "    module = reload(module)\n",
-      "  File \"/Users/nathanielforde/mambaforge/envs/CausalPy/lib/python3.13/importlib/__init__.py\", line 128, in reload\n",
-      "    raise ModuleNotFoundError(f\"spec not found for the module {name!r}\", name=name)\n",
-      "ModuleNotFoundError: spec not found for the module 'cutils_ext'\n",
-      "]\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
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        "    
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        "    
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        "      1\n",
-       "      0.847573\n",
-       "      62.105904\n",
-       "      0.038199\n",
+       "      0.697739\n",
+       "      57.713961\n",
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        "    
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        "    
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        "      2\n",
-       "      0.164625\n",
-       "      12.062918\n",
-       "      0.038199\n",
+       "      0.160282\n",
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        "    
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        "    
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        "      3\n",
-       "      0.136282\n",
-       "      9.986066\n",
-       "      0.038199\n",
+       "      0.129649\n",
+       "      10.724023\n",
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        "    
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        "    
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        "      4\n",
-       "      0.090871\n",
-       "      6.658563\n",
-       "      0.038199\n",
+       "      0.076676\n",
+       "      6.342298\n",
+       "      0.036811\n",
        "    
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        "    
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        "      5\n",
-       "      0.030253\n",
-       "      2.216801\n",
-       "      0.038199\n",
+       "      0.025650\n",
+       "      2.121680\n",
+       "      0.036811\n",
        "    
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        "    
\n",
        "      6\n",
-       "      0.199524\n",
-       "      14.620131\n",
-       "      0.038199\n",
+       "      0.190186\n",
+       "      15.731398\n",
+       "      0.036811\n",
        "    
\n",
        "    
\n",
        "      7\n",
-       "      0.015415\n",
-       "      1.129522\n",
-       "      0.038199\n",
+       "      0.014198\n",
+       "      1.174382\n",
+       "      0.036811\n",
        "    
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        "    
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        "      8\n",
-       "      0.015330\n",
-       "      1.123328\n",
-       "      0.038199\n",
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+       "      0.036811\n",
        "    
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        "    
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        "      9\n",
-       "      0.015992\n",
-       "      1.171778\n",
-       "      0.038199\n",
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        "    
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        "    
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        "    
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        "      11\n",
-       "      0.020536\n",
-       "      1.504805\n",
-       "      0.038199\n",
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+       "      0.036811\n",
        "    
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        "    
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        "      12\n",
-       "      0.021961\n",
-       "      1.609202\n",
-       "      0.038199\n",
+       "      0.018518\n",
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+       "      0.036811\n",
        "    
\n",
        "    
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        "      13\n",
-       "      0.020631\n",
-       "      1.511758\n",
-       "      0.038199\n",
+       "      0.017415\n",
+       "      1.440514\n",
+       "      0.036811\n",
        "    
\n",
        "    
\n",
        "      14\n",
-       "      0.021207\n",
-       "      1.553953\n",
-       "      0.038199\n",
+       "      0.019238\n",
+       "      1.591291\n",
+       "      0.036811\n",
        "    
\n",
        "    
\n",
        "      15\n",
-       "      0.019708\n",
-       "      1.444109\n",
-       "      0.038199\n",
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+       "      1.445946\n",
+       "      0.036811\n",
        "    
\n",
        "  \n",
        "\n",
@@ -1899,25 +1948,25 @@
       ],
       "text/plain": [
        "    shrinkage_factor  lambda_tilde       tau\n",
-       "0           0.016681      1.222294  0.038199\n",
-       "1           0.847573     62.105904  0.038199\n",
-       "2           0.164625     12.062918  0.038199\n",
-       "3           0.136282      9.986066  0.038199\n",
-       "4           0.090871      6.658563  0.038199\n",
-       "5           0.030253      2.216801  0.038199\n",
-       "6           0.199524     14.620131  0.038199\n",
-       "7           0.015415      1.129522  0.038199\n",
-       "8           0.015330      1.123328  0.038199\n",
-       "9           0.015992      1.171778  0.038199\n",
-       "10          0.015676      1.148635  0.038199\n",
-       "11          0.020536      1.504805  0.038199\n",
-       "12          0.021961      1.609202  0.038199\n",
-       "13          0.020631      1.511758  0.038199\n",
-       "14          0.021207      1.553953  0.038199\n",
-       "15          0.019708      1.444109  0.038199"
+       "0           0.014780      1.222524  0.036811\n",
+       "1           0.697739     57.713961  0.036811\n",
+       "2           0.160282     13.257798  0.036811\n",
+       "3           0.129649     10.724023  0.036811\n",
+       "4           0.076676      6.342298  0.036811\n",
+       "5           0.025650      2.121680  0.036811\n",
+       "6           0.190186     15.731398  0.036811\n",
+       "7           0.014198      1.174382  0.036811\n",
+       "8           0.014636      1.210634  0.036811\n",
+       "9           0.014024      1.160002  0.036811\n",
+       "10          0.014441      1.194485  0.036811\n",
+       "11          0.018907      1.563934  0.036811\n",
+       "12          0.018518      1.531734  0.036811\n",
+       "13          0.017415      1.440514  0.036811\n",
+       "14          0.019238      1.591291  0.036811\n",
+       "15          0.017481      1.445946  0.036811"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1931,23 +1980,149 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 53,
    "id": "82b0121c",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
-       ""
+       "
"
       ]
      },
-     "execution_count": 14,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(20, 6))\n",
+    "az.plot_posterior(\n",
+    "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    result_horseshoe.idata,\n",
+    "    var_names=[\"beta_z\"],\n",
+    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    ax=ax,\n",
+    "    color=\"green\",\n",
+    ")\n",
+    "ax.axvline(3, color=\"black\", linestyle=\"--\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e15d4f1e",
+   "metadata": {},
+   "source": [
+    "In this case it seems the horseshoe prior leads a bi-modal posterior estimate of the treatment effect suggesting a kind of indecision about the level of sparsity to apply. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc265f5d",
+   "metadata": {},
+   "source": [
+    "## Binary Treatment Case\n",
+    "\n",
+    "Our data generating function output two different simulation scenarios, where the treatment was either continuous or binary. This allows us to demonstrate the joint modelling of the binary treatment outcome which uses a Bernoulli likelihood for the treatment variable and latent confounding to model the joint realisation of treatment and outcome. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "89e61d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Model 1: Normal Priors Binary Treatment (No Variable Selection)\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [beta_t, beta_z, sigma_U, rho_unconstr, eps_raw]\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c7276bf767824813a312c4c3ccff7c01",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
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",
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 68 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "formula = \"Y_bin ~ T_bin + \" + \" + \".join(features)\n",
+    "instruments_formula = \"T_bin ~ 1 + \" + \" + \".join(features)\n",
+    "\n",
+    "\n",
+    "# =========================================================================\n",
+    "# Model 1: Normal priors (no selection)\n",
+    "# =========================================================================\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"Model 1: Normal Priors Binary Treatment (No Variable Selection)\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "result_normal_binary = cp.InstrumentalVariable(\n",
+    "    instruments_data=instruments_data,\n",
+    "    data=data,\n",
+    "    instruments_formula=instruments_formula,\n",
+    "    formula=formula,\n",
+    "    model=cp.pymc_models.InstrumentalVariableRegression(sample_kwargs=sample_kwargs),\n",
+    "    vs_prior_type=None,  # No variable selection\n",
+    "    binary_treatment=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "98c1b50a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "
"
       ]
@@ -1962,12 +2137,47 @@
     "    result_normal.idata, var_names=[\"beta_z\"], coords={\"covariates\": [\"T_cont\"]}, ax=ax\n",
     ")\n",
     "az.plot_posterior(\n",
-    "    result_horseshoe.idata,\n",
+    "    result_normal_binary.idata,\n",
     "    var_names=[\"beta_z\"],\n",
-    "    coords={\"covariates\": [\"T_cont\"]},\n",
+    "    coords={\"covariates\": [\"T_bin\"]},\n",
     "    ax=ax,\n",
+    "    color=\"green\",\n",
     ")\n",
-    "ax.axvline(3, color=\"black\", linestyle=\"--\")"
+    "ax.axvline(3, color=\"black\", linestyle=\"--\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36ad018b",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Conclusion: Choosing Your Path Through Uncertainty\n",
+    "\n",
+    "Variable selection priors offer a principled way to navigate the tension between model complexity and causal identification. Rather than forcing binary decisions about which variables to include, these priors encode our uncertainty about variable relevance directly into the inferential framework. But as we've seen, the choice between spike-and-slab and horseshoe reflects deeper commitments about how sparsity manifests in the world.\n",
+    "\n",
+    "**The spike-and-slab prior** embodies decisiveness. It asks: which variables truly matter? By pushing coefficients toward exactly zero or allowing them to take on substantial values, it produces interpretable inclusion probabilities that clearly communicate which predictors the model has \"selected.\" This approach shines when you believe that many potential confounders are genuine noise—included out of caution but ultimately irrelevant to the causal mechanism. The discrete nature of selection also makes results easier to communicate to stakeholders who think in terms of \"what factors matter?\" \n",
+    "\n",
+    "**The horseshoe prior** embraces nuance. It acknowledges that effects exist on a continuum, and that small but real contributions shouldn't be entirely zeroed out. The continuous shrinkage allows weak signals to persist (heavily damped) while strong signals emerge largely unscathed. This is valuable when you suspect that multiple confounders have genuine but varying degrees of influence, and when premature exclusion of any single variable might introduce bias. The regularization parameter $c^2$ acts as a safeguard, preventing even the horseshoe's aggressive shrinkage from overwhelming genuinely large effects.\n",
+    " \n",
+    "In our simulations, both approaches identified the true treatment effect of 3, though they arrived there differently. The spike-and-slab showed more confidence, producing tighter posterior intervals by decisively excluding noise variables. The horseshoe's bi-modal posterior in some specifications revealed its uncertainty about the appropriate level of sparsity a kind of probabilistic humility that spike-and-slab's discrete choices don't allow.\n",
+    "\n",
+    "**Practical Guidance:**\n",
+    " \n",
+    "- **Use spike-and-slab when** you have strong priors about sparsity (many potential confounders, few true ones), when interpretability matters (stakeholders want to know \"what's included?\"), or when you're willing to trade some flexibility for more decisive inference.\n",
+    " \n",
+    "- **Use horseshoe when** you're uncertain about sparsity levels, when small effects might still matter for causal identification, or when you want the model to smoothly adapt its shrinkage to the data without hard inclusion/exclusion decisions.\n",
+    " \n",
+    "- **Use neither when** theory clearly identifies your confounders, when sample size is large relative to the number of predictors, or when the cost of Type I errors (including irrelevant variables) is low relative to Type II errors (excluding true confounders).\n",
+    " \n",
+    "**Final Thoughts:**\n",
+    " \n",
+    "Variable selection priors don't eliminate the need for causal reasoning. They don't tell you which variables are *causally* relevant, only which are *statistically* predictive. But when used thoughtfully—guided by theory about potential confounders, informed by domain knowledge about likely sparsity patterns, and validated through sensitivity analysis. They offer a middle path between the Scylla of over-specification (including everything) and the Charybdis of under-specification (excluding too much). Used within a joint model of treatment and outcome variable, the argument of a variable selection routine  represents an attempt to calibrate the parameters to select the instrument structure. What variable selection is really doing in joint treatment-outcome models is calibrating the parameters to discover patterns consistent with instrument structure *if such structure exists in the data*. The horseshoe shrinks away coefficients that appear redundant given the covariance structure between treatment, outcome, and covariates. The spike-and-slab actively excludes variables that don't contribute to explaining either margin after accounting for shared variation.\n",
+    " \n",
+    "The ideal use of variable selection in instrumental variable designs is not as a replacement for domain knowledge but as a consistency check. The real power of these methods lies not in automation but in transparency. By making variable selection part of the posterior distribution rather than a pre-processing step, we can quantify and communicate our uncertainty about model structure itself. This moves us closer to the goal of all principled causal inference: not just estimating effects, but understanding the limits of what we can learn from the data we have.\n",
+    " \n",
+    "As always in causal inference, the model is a question posed to the data. Variable selection priors help us ask that question more precisely, but we still need theory to tell us if we're asking the right question at all.\n",
+    "\n"
    ]
   }
  ],

From bf5b40414bb6b51a25aae31df6b47e8c45225c0e Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Wed, 26 Nov 2025 21:44:48 +0000
Subject: [PATCH 16/17] update heading

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 3e293ad5..9f830e89 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -2023,7 +2023,7 @@
    "id": "fc265f5d",
    "metadata": {},
    "source": [
-    "## Binary Treatment Case\n",
+    "### Binary Treatment Case\n",
     "\n",
     "Our data generating function output two different simulation scenarios, where the treatment was either continuous or binary. This allows us to demonstrate the joint modelling of the binary treatment outcome which uses a Bernoulli likelihood for the treatment variable and latent confounding to model the joint realisation of treatment and outcome. "
    ]

From b16ef30a24660431ae456675d7a0b9bb494cc6ab Mon Sep 17 00:00:00 2001
From: Nathaniel 
Date: Wed, 26 Nov 2025 21:51:03 +0000
Subject: [PATCH 17/17] hide cells

Signed-off-by: Nathaniel 
---
 docs/source/notebooks/iv_vs_priors.ipynb | 22 ++++++++++++++++++----
 1 file changed, 18 insertions(+), 4 deletions(-)

diff --git a/docs/source/notebooks/iv_vs_priors.ipynb b/docs/source/notebooks/iv_vs_priors.ipynb
index 9f830e89..a3faa0c1 100644
--- a/docs/source/notebooks/iv_vs_priors.ipynb
+++ b/docs/source/notebooks/iv_vs_priors.ipynb
@@ -586,7 +586,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "763ca253",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1438,7 +1442,9 @@
    "id": "f09b24bf",
    "metadata": {},
    "source": [
-    "#### The Treatment Model"
+    "#### The Treatment Model\n",
+    "\n",
+    "Variable selection is applied to both the outcome and the treatment model. In this way we calibrate our parameters to the joint patterns of realisations between these two endogenous variables. "
    ]
   },
   {
@@ -1706,7 +1712,11 @@
    "cell_type": "code",
    "execution_count": 39,
    "id": "63edfa4e",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2032,7 +2042,11 @@
    "cell_type": "code",
    "execution_count": 44,
    "id": "89e61d28",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide-output"
+    ]
+   },
    "outputs": [
     {
      "name": "stdout",