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.
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============== =============================================
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 jaakko.luttinen@iki.fi
Chat |chat|
Mailing list bayespy@googlegroups.com
============== =============================================
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/>`_
(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/>`_
(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/>`_
(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>`_
(https://github.com/pymc-devs/pymc) provides MCMC methods in Python.
It is released under the Academic Free License.
`OpenBUGS <http://www.openbugs.info>`_ (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/>`_ (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/>`_ (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/>`_
(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.
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"description": "BayesPy - Bayesian Python\n=========================\n\nBayesPy provides tools for Bayesian inference with Python. The user\nconstructs a model as a Bayesian network, observes data and runs\nposterior inference. The goal is to provide a tool which is\nefficient, flexible and extendable enough for expert use but also\naccessible for more casual users.\n\nCurrently, only variational Bayesian inference for\nconjugate-exponential family (variational message passing) has been\nimplemented. Future work includes variational approximations for\nother types of distributions and possibly other approximate inference\nmethods such as expectation propagation, Laplace approximations,\nMarkov chain Monte Carlo (MCMC) and other methods. Contributions are\nwelcome.\n\n\nProject information\n-------------------\n\nCopyright (C) 2011-2017 Jaakko Luttinen and other contributors (see below)\n\nBayesPy including the documentation is licensed under the MIT License. See\nLICENSE file for a text of the license or visit\nhttp://opensource.org/licenses/MIT.\n\n.. |chat| image:: https://badges.gitter.im/Join%20Chat.svg\n :target: https://gitter.im/bayespy/bayespy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge\n.. |release| image:: https://badge.fury.io/py/bayespy.svg\n :target: https://pypi.python.org/pypi/bayespy\n.. |conda-release| image:: https://anaconda.org/conda-forge/bayespy/badges/installer/conda.svg\n :target: https://anaconda.org/conda-forge/bayespy\n\n============== =============================================\nLatest release |release| |conda-release|\nDocumentation http://bayespy.org\nRepository https://github.com/bayespy/bayespy.git\nBug reports https://github.com/bayespy/bayespy/issues\nAuthor Jaakko Luttinen jaakko.luttinen@iki.fi\nChat |chat|\nMailing list bayespy@googlegroups.com\n============== =============================================\n\n\nContinuous integration\n++++++++++++++++++++++\n\n.. |travismaster| image:: https://travis-ci.org/bayespy/bayespy.svg?branch=master\n :target: https://travis-ci.org/bayespy/bayespy/\n :align: middle\n.. |travisdevelop| image:: https://travis-ci.org/bayespy/bayespy.svg?branch=develop\n :target: https://travis-ci.org/bayespy/bayespy/\n :align: middle\n.. |covermaster| image:: https://coveralls.io/repos/bayespy/bayespy/badge.svg?branch=master\n :target: https://coveralls.io/r/bayespy/bayespy?branch=master\n :align: middle\n.. |coverdevelop| image:: https://coveralls.io/repos/bayespy/bayespy/badge.svg?branch=develop\n :target: https://coveralls.io/r/bayespy/bayespy?branch=develop\n :align: middle\n.. |docsmaster| image:: https://img.shields.io/badge/docs-master-blue.svg?style=flat\n :target: http://www.bayespy.org/en/stable/\n :align: middle\n.. |docsdevelop| image:: https://img.shields.io/badge/docs-develop-blue.svg?style=flat\n :target: http://www.bayespy.org/en/latest/\n :align: middle\n\n==================== =============== ============== =============\nBranch Test status Test coverage Documentation\n==================== =============== ============== =============\n**master (stable)** |travismaster| |covermaster| |docsmaster|\n**develop (latest)** |travisdevelop| |coverdevelop| |docsdevelop|\n==================== =============== ============== =============\n\n\nSimilar projects\n----------------\n\n`VIBES <http://vibes.sourceforge.net/>`_\n(http://vibes.sourceforge.net/) allows variational inference to be\nperformed automatically on a Bayesian network. It is implemented in\nJava and released under revised BSD license.\n\n`Bayes Blocks <http://research.ics.aalto.fi/bayes/software/>`_\n(http://research.ics.aalto.fi/bayes/software/) is a C++/Python\nimplementation of the variational building block framework. The\nframework allows easy learning of a wide variety of models using\nvariational Bayesian learning. It is available as free software under\nthe GNU General Public License.\n\n`Infer.NET <http://research.microsoft.com/infernet/>`_\n(http://research.microsoft.com/infernet/) is a .NET framework for\nmachine learning. It provides message-passing algorithms and\nstatistical routines for performing Bayesian inference. It is partly\nclosed source and licensed for non-commercial use only.\n\n`PyMC <https://github.com/pymc-devs/pymc>`_\n(https://github.com/pymc-devs/pymc) provides MCMC methods in Python.\nIt is released under the Academic Free License.\n\n`OpenBUGS <http://www.openbugs.info>`_ (http://www.openbugs.info) is a\nsoftware package for performing Bayesian inference using Gibbs\nsampling. It is released under the GNU General Public License.\n\n`Dimple <http://dimple.probprog.org/>`_ (http://dimple.probprog.org/) provides\nGibbs sampling, belief propagation and a few other inference algorithms for\nMatlab and Java. It is released under the Apache License.\n\n`Stan <http://mc-stan.org/>`_ (http://mc-stan.org/) provides inference using\nMCMC with an interface for R and Python. 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