ML Anthology

PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python

Jonathan Serrano-Pérez, L. Enrique Sucar
PGM 2020 pp. 625-628
/pgm/2020/serranoperez2020pgm-pgm/

Abstract

PGM{_}PyLib is a toolkit that contains a wide range of Probabilistic Graphical Models algorithms implemented in Python, and serves as a companion of the book Probabilistic Graphical Models: Principles and Applications. Currently, the algorithms implemented include: Bayesian classifiers, hidden Markov models, Markov random fields, and Bayesian networks; as well as some general functions. The toolkit is open source, can be downloaded from: https://github.com/jona2510/PGM{_}PyLib .

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Cite

Text

Serrano-Pérez and Sucar. "PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python." Proceedings of pgm 2020, 2020.

Markdown

[Serrano-Pérez and Sucar. "PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python." Proceedings of pgm 2020, 2020.](https://mlanthology.org/pgm/2020/serranoperez2020pgm-pgm/)

BibTeX

@inproceedings{serranoperez2020pgm-pgm,
  title     = {{PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python}},
  author    = {Serrano-Pérez, Jonathan and Sucar, L. Enrique},
  booktitle = {Proceedings of pgm 2020},
  year      = {2020},
  pages     = {625-628},
  volume    = {138},
  url       = {https://mlanthology.org/pgm/2020/serranoperez2020pgm-pgm/}
}