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Metadata-Version: 2.1
Name: bayespy
Version: 0.5.18
Summary: Variational Bayesian inference tools for Python
Home-page: http://bayespy.org
Author: Jaakko Luttinen
Author-email: jaakko.luttinen@iki.fi
License: UNKNOWN
Description: 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
           :target: https://gitter.im/bayespy/bayespy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
        .. |release| image:: https://badge.fury.io/py/bayespy.svg
           :target: https://pypi.python.org/pypi/bayespy
        
        ============== =============================================
        Latest release |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
        ++++++++++++++++++++++
        
        .. |travismaster| image:: https://travis-ci.org/bayespy/bayespy.svg?branch=master
           :target: https://travis-ci.org/bayespy/bayespy/
           :align: middle
        .. |travisdevelop| image:: https://travis-ci.org/bayespy/bayespy.svg?branch=develop
           :target: https://travis-ci.org/bayespy/bayespy/
           :align: middle
        .. |covermaster| image:: https://coveralls.io/repos/bayespy/bayespy/badge.svg?branch=master
           :target: https://coveralls.io/r/bayespy/bayespy?branch=master
           :align: middle
        .. |coverdevelop| image:: https://coveralls.io/repos/bayespy/bayespy/badge.svg?branch=develop
           :target: https://coveralls.io/r/bayespy/bayespy?branch=develop
           :align: middle
        .. |docsmaster| image:: https://img.shields.io/badge/docs-master-blue.svg?style=flat
           :target: http://www.bayespy.org/en/stable/
           :align: middle
        .. |docsdevelop| image:: https://img.shields.io/badge/docs-develop-blue.svg?style=flat
           :target: http://www.bayespy.org/en/latest/
           :align: middle
        
        ==================== =============== ============== =============
        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.
        
Keywords: variational Bayes,probabilistic programming,Bayesian networks,graphical models,variational message passing
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Provides-Extra: doc
Provides-Extra: dev