Drew Herren's Projects
Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"
Drew Herren's personal webpage
API for stochastic tree ensembles using FastAPI for AWS Lamda deployment.
BART and Posterior Summarization Reading Group at UT Austin
Bayesian Causal Forests
Source code and data for The Economist's covid-19 excess deaths tracker
The Economist's model to estimate excess deaths to the covid-19 pandemic
Discrete Bayesian Additive Regression Trees Sampler
Code for experiments and simulation studies run in my PhD dissertation
Analysis of several techniques for model explainability
Adventures in Hofstadter's Goedel, Escher, Bach
Implementations of common algorithms / statistical methods for didactic purposes
Minimalist C++ implementation of decision tree algorithms
Initial explorations in natural language processing
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Model explainability using the OSCAR algorithm
Probabilistic Modeling Toolkit for Matlab/Octave.
POSterior SUMmarization
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Design of experiments for Python
Python code for "Machine learning: a probabilistic perspective"
The "Python Machine Learning (1st edition)" book code repository and info resource
Statistical Rethinking course and book package
scikit-learn: machine learning in Python
Flexible library for implementing and experimenting with tree methods, based on the scikit-learn tree codebase