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Probabilistic graphical models in python
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About ----- Probabilistic graphical models in python. This code is intended mainly as proof of concept of the algorithms presented in . The implementations are not particularly clear, efficient, well tested or numerically stable. We advise against using this software for nondidactic purposes. This software is licensed under the MIT License. Features -------- Models: Bayesian network (table conditional probability distributions) Markov network (table potentials) Influence diagram Inference: Variable elimination Forward sampling Gibbs sampling Learning: Parameter learning (maximum likelihood, uniform BDe, expectation maximization for missing data) Structure learning (local search, likelihood score, BIC score, Bayesian score) Examples -------- See the examples directory. References ----------  Koller, D. and Friedman, N. Probabilistic Graphical Models: Principles and Techniques. The MIT Press. 2009.