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https://github.com/bayespy/bayespy

Bayesian Python: Bayesian inference tools for Python
https://github.com/bayespy/bayespy

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Bayesian Python: Bayesian inference tools for Python

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README 

        
BayesPy - Bayesian Python

=========================



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.



Project information

-------------------



Copyright (C) 2011-2017 Jaakko Luttinen and other contributors (see below)



BayesPy including the documentation is licensed under the MIT License. See

LICENSE file for a text of the license or visit

http://opensource.org/licenses/MIT.



.. |chat| image:: https://badges.gitter.im/Join%20Chat.svg

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.. |release| image:: https://badge.fury.io/py/bayespy.svg

   :target: https://pypi.python.org/pypi/bayespy

.. |conda-release| image:: https://anaconda.org/conda-forge/bayespy/badges/installer/conda.svg

   :target: https://anaconda.org/conda-forge/bayespy



============== =============================================

Latest release |release| |conda-release|

Documentation  http://bayespy.org

Repository     https://github.com/bayespy/bayespy.git

Bug reports    https://github.com/bayespy/bayespy/issues

Author         Jaakko Luttinen [email protected]

Chat           |chat|

Mailing list   [email protected]

============== =============================================



Continuous integration

++++++++++++++++++++++



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==================== =============== ============== =============

Branch               Test status     Test coverage  Documentation

==================== =============== ============== =============

**master (stable)**  |travismaster|  |covermaster|  |docsmaster|

**develop (latest)** |travisdevelop| |coverdevelop| |docsdevelop|

==================== =============== ============== =============



Similar projects

----------------



`VIBES `_

(http://vibes.sourceforge.net/) allows variational inference to be

performed automatically on a Bayesian network.  It is implemented in

Java and released under revised BSD license.



`Bayes Blocks `_

(http://research.ics.aalto.fi/bayes/software/) is a C++/Python

implementation of the variational building block framework.  The

framework allows easy learning of a wide variety of models using

variational Bayesian learning.  It is available as free software under

the GNU General Public License.



`Infer.NET `_

(http://research.microsoft.com/infernet/) is a .NET framework for

machine learning.  It provides message-passing algorithms and

statistical routines for performing Bayesian inference.  It is partly

closed source and licensed for non-commercial use only.



`PyMC `_

(https://github.com/pymc-devs/pymc) provides MCMC methods in Python.

It is released under the Academic Free License.



`OpenBUGS `_ (http://www.openbugs.info) is a

software package for performing Bayesian inference using Gibbs

sampling.  It is released under the GNU General Public License.



`Dimple `_ (http://dimple.probprog.org/) provides

Gibbs sampling, belief propagation and a few other inference algorithms for

Matlab and Java.  It is released under the Apache License.



`Stan `_ (http://mc-stan.org/) provides inference using

MCMC with an interface for R and Python.  It is released under the New BSD

License.



`PBNT - Python Bayesian Network Toolbox `_

(http://pbnt.berlios.de/) is Bayesian network library in Python supporting

static networks with discrete variables.  There was no information about the

license.



Contributors

------------



The list of contributors:



* Jaakko Luttinen



* Hannu Hartikainen



* Deebul Nair



* Christopher Cramer



* Till Hoffmann



Each file or the git log can be used for more detailed information.