pgmpy: A Python Toolkit for Bayesian Networks

Ankur Ankan, Johannes Textor
4/17/2023
cs.LGstat.ME

Official code: pgmpy/pgmpy (3,305 stars, MIT, last commit 2026-05-03).

Abstract

Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, causal inference, and simulations. These implementations focus on modularity and easy extensibility to allow users to quickly modify/add to existing algorithms, or to implement new algorithms for different use cases. pgmpy is released under the MIT License; the source code is available at: https://github.com/pgmpy/pgmpy, and the documentation at: https://pgmpy.org.

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Code Implementations(1)

pgmpy/pgmpyOfficial100%
MIT

Python Toolkit for Causal and Probabilistic Reasoning

3,3051,136R, PythonSep 20, 20135 months agoMIT
bayesian-networkscausal-discoverycausal-effectcausal-identificationcausal-inference+10 more

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Cite this paper

@article{ankan2023pgmpy,
  title  = {pgmpy: A Python Toolkit for Bayesian Networks},
  author = {Ankur Ankan and Johannes Textor},
  year   = {2023},
  eprint = {2304.08639},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.08639v1}
}

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