2020
PGM
PGM 2020
PGM_PyLib: A Toolkit for Probabilistic Graphical Models in Python
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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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio