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.
Copyright (C) 2011-2014 Jaakko Luttinen, Aalto University
BayesPy including the documentation is licensed under Version 3.0 of the GNU General Public License. See LICENSE file for a text of the license or visit http://www.gnu.org/copyleft/gpl.html.
- Documentation: http://bayespy.org or at doc directory
- Repository: https://github.com/bayespy/bayespy.git
- Bug reports: https://github.com/bayespy/bayespy/issues
- Mailing list: firstname.lastname@example.org
- IRC: #bayespy @ freenode
- Author: Jaakko Luttinen email@example.com
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