The Probabilistic Graphical Models Python Library (PGM_PyLib) was written for inference and learning of several classes of Probabilistic Graphical Models (PGM) in Python. The theory behind the different algorithms can be found in the book Probabilistic Graphical Models Principles and Applications of Luis Enrique Sucar.

PGM_PyLib include several algorithms based on a graphical representation of independence relations, such as:

• Bayesian Classifiers
  • Naive Bayes Classifier
  • Gaussian Naive Bayes Classifier
  • Bayesian Network augmented Bayesian Classifier (BAN)
  • Semi Naive Bayes Classifier
  • Bayesian Chain Classifier
  • Hierarchical classification with Bayesian Networks and Chained Classifiers
• Hidden Markov Models
• Markov Random Models
• Bayesian Networks
  • Chow-Liu procedure (CLP)
  • CLP with Conditional Mutual information
  • PC algorithm
• Markov Decision Processes

Please check the manual for the full list of algorithms.

The "PGM_PyLib Manual vX.X.pdf" contains the description of the PGM's which were implemented, also you will find different examples.

If you use the library, please cite us.

From this work, we published the paper PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python:

@InProceedings{pgm-pylib,
	title = {PGM{\_}PyLib: A Toolkit for Probabilistic Graphical Models in Python},
	author = {Serrano-⁠P{\'e}rez, Jonathan and Sucar, L. Enrique},
	booktitle = {The 10th International Conference on Probabilistic Graphical Models}, 
	year = 2020,
	month = September,
	address = {Aalborg, Denmark}
}

Please, also cite the manual and the book.

@manual{pgm-pylib-manual,
	title = {PGM_PyLib: A Python Library for Inference and Learning of Probabilistic Graphical Models},
	author = {Serrano-⁠P{\'e}rez, Jonathan and Sucar, L. Enrique},
	year = 2020
}

@book{pgm-book, 
    author = {L. Enrique Sucar}, year = 2015,
    title = {Probabilistic Graphical Models Principles and Applications }, 
    edition = 1, 
    publisher = {Springer-Verlag London}, 
    adress = {London} 
}