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Author: alicjapolanska

README.rst

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-``harmonic`` is an open source, well tested and documented Python implementation of the *learnt harmonic mean estimator* (`McEwen et al. 2021 <https://arxiv.org/abs/2111.12720>`_) to compute the marginal likelihood (Bayesian evidence), required for Bayesian model selection.
+``harmonic`` is an open source, well tested and documented Python implementation of the *learned harmonic mean estimator* (`McEwen et al. 2021 <https://arxiv.org/abs/2111.12720>`_) to compute the marginal likelihood (Bayesian evidence), required for Bayesian model selection.
 
-For an accessible overview of the *learnt harmonic mean estimator* please see this `Towards Data Science article <https://towardsdatascience.com/learnt-harmonic-mean-estimator-for-bayesian-model-selection-47258bb0fc2e>`_.
+For an accessible overview of the *learned harmonic mean estimator* please see this `Towards Data Science article <https://towardsdatascience.com/learnt-harmonic-mean-estimator-for-bayesian-model-selection-47258bb0fc2e>`_.
 
 While ``harmonic`` requires only posterior samples, and so is agnostic to the technique used to perform Markov chain Monte Carlo (MCMC) sampling, ``harmonic`` works well with MCMC sampling techniques that naturally provide samples from multiple chains by their ensemble nature, such as affine invariant ensemble samplers.  For instance, ``harmonic`` can be used with the popular `emcee <https://github.com/dfm/emcee>`_ code implementing the affine invariant sampler of `Goodman & Weare (2010) <https://cims.nyu.edu/~weare/papers/d13.pdf>`_, or the `NumPyro <https://github.com/pyro-ppl/numpyro>`_ code implementing various MCMC algorithms.