PGM_PyLib: A Toolkit for Probabilistic Graphical Models 
 in Python

Jonathan Serrano-Pérez; L. Enrique Sucar

PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python

Jonathan Serrano-Pérez, L. Enrique Sucar

Proceedings of the 10th International Conference on Probabilistic Graphical Models, PMLR 138:625-628, 2020.

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 .

Cite this Paper

BibTeX


@InProceedings{pmlr-v138-serrano-perez20a,
  title = 	 {PGM{_}PyLib: A Toolkit for Probabilistic Graphical Models 
 in Python},
  author =       {Serrano-P{\'e}rez, Jonathan and Sucar, L. Enrique},
  booktitle = 	 {Proceedings of the 10th International Conference on Probabilistic Graphical Models},
  pages = 	 {625--628},
  year = 	 {2020},
  editor = 	 {Jaeger, Manfred and Nielsen, Thomas Dyhre},
  volume = 	 {138},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--25 Sep},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v138/serrano-perez20a/serrano-perez20a.pdf},
  url = 	 {https://proceedings.mlr.press/v138/serrano-perez20a.html},
  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 .}
}

Endnote

%0 Conference Paper
%T PGM_PyLib: A Toolkit for Probabilistic Graphical Models 
 in Python
%A Jonathan Serrano-Pérez
%A L. Enrique Sucar
%B Proceedings of the 10th International Conference on Probabilistic Graphical Models
%C Proceedings of Machine Learning Research
%D 2020
%E Manfred Jaeger
%E Thomas Dyhre Nielsen	
%F pmlr-v138-serrano-perez20a
%I PMLR
%P 625--628
%U https://proceedings.mlr.press/v138/serrano-perez20a.html
%V 138
%X 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 .

APA


Serrano-Pérez, J. & Sucar, L.E.. (2020). PGM_PyLib: A Toolkit for Probabilistic Graphical Models 
 in Python. Proceedings of the 10th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 138:625-628 Available from https://proceedings.mlr.press/v138/serrano-perez20a.html.

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