Variational Bayesian inference tools for Python


Info: This package contains files in non-standard labels.
  • noarch v0.5.26

conda install

To install this package run one of the following:
conda install -c conda-forge bayespy
conda install -c "conda-forge/label/cf202003" bayespy


BayesPy provides tools for Bayesian inference with Python. The user constructs a model as a Bayesian network, observes data and runs posterior inference. The goal is to provide a tool which is efficient, flexible and extendable enough for expert use but also accessible for more casual users.

Currently, only variational Bayesian inference for conjugate-exponential family (variational message passing) has been implemented. Future work includes variational approximations for other types of distributions and possibly other approximate inference methods such as expectation propagation, Laplace approximations, Markov chain Monte Carlo (MCMC) and other methods. Contributions are welcome.

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