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 = {http://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 http://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 http://proceedings.mlr.press/v138/serrano-perez20a.html.

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