Taking causal inference to the extreme!
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Updated
Dec 17, 2024 - Julia
Taking causal inference to the extreme!
Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks (TKDD 2019)
Simple Nonparametric Reinforcement Learning with Universal Interface
Created a model to estimate the measurement error existing in ROC curves - Measurement Error model, Bernstein polynomial Model, Contaminated Non-parametric Density Estimation, MLE, EM Algorithm, R
Tool for generating random variables that follows given frequency distibution using linear regressions in given intervals.
An Open-Source Python framework for Locally Weighted Regression and Classification, built on PyTorch and Scikit-Learn
Samples of my work in programming and data analysis.
🛠「NopaPy」是一个开源易用的非参数统计Python库。
This project examines how weather and seasonal factors shape U.S. electricity consumption using parametric GLMs and nonparametric Random Forest. It then incorporates state and lag features to build stronger ensemble models and compare their performance.
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