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Bayesian Additive Regression Trees for Probabilistic programming with PyMC


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.


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


Alternatively, if you want the bleeding edge version of the package you can install from GitHub:

pip install git+


If you use PyMC-BART and want to cite it please use arXiv

Here is the citation in BibTeX format

  doi = {10.48550/ARXIV.2206.03619},
  url = {},
  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}


We welcome contributions from interested individuals or groups! For information about contributing to PyMC-BART check out our instructions, policies, and guidelines here.


See the GitHub contributor page.