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Probabilistic graphical models in python

This code is intended mainly as proof of concept of the algorithms presented in [1]. 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

[1] Koller, D. and Friedman, N. Probabilistic Graphical Models: Principles and Techniques. The MIT Press. 2009.