PyMC-BART#
Bayesian Additive Regression Trees for Probabilistic programming with PyMC
Overview#
PyMC-BART extends PyMC probabilistic programming framework to be able to define and solve models including a BART random variable. PyMC-BART also includes a few helpers function to aid with the interpretation of those models and perform variable selection.
Installation#
PyMC-BART requires a working Python interpreter (3.8+). We recommend installing Python and key numerical libraries using the Anaconda distribution, which has one-click installers available on all major platforms.
Assuming a standard Python environment is installed on your machine, PyMC-BART itself can be installed either using pip or conda-forge.
Using pip
pip install pymc-bart
Using conda-forge
conda install -c conda-forge pymc-bart
Development
Alternatively, if you want the bleeding edge version of the package you can install from GitHub:
pip install git+https://github.com/pymc-devs/pymc-bart.git
Citation#
If you use PyMC-BART and want to cite it please use
Here is the citation in BibTeX format
@misc{quiroga2022bart,
doi = {10.48550/ARXIV.2206.03619},
url = {https://arxiv.org/abs/2206.03619},
author = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
keywords = {Computation (stat.CO), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Bayesian additive regression trees for probabilistic programming},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}
Contributing#
We welcome contributions from interested individuals or groups! For information about contributing to PyMC-BART check out our instructions, policies, and guidelines here.
Contributors#
See the GitHub contributor page.