Metadata-Version: 1.1
Name: pypmc
Version: 0.9
Summary: A toolkit for adaptive importance sampling featuring implementations of variational Bayes and population Monte Carlo.
Home-page: https://github.com/fredRos/pypmc
Author: Frederik Beaujean, Stephan Jahn
Author-email: Frederik.Beaujean@lmu.de, stephan.jahn@mytum.de
License: GPLv2
Description: ``pypmc`` is a python package focusing on adaptive importance
        sampling.  It can be used for integration and sampling from a
        user-defined target density. A typical application is Bayesian
        inference, where one wants to sample from the posterior to marginalize
        over parameters and to compute the evidence. The key idea is to create
        a good proposal density by adapting a mixture of Gaussian or student's
        t components to the target density. The package is able to efficiently
        integrate multimodal functions in up to about 30-40 dimensions at the
        level of 1% accuracy or less. For many problems, this is achieved
        without requiring any manual input from the user about details of the
        function.
        
        Useful tools that can be used stand-alone include:
        
        * importance sampling (sampling & integration)
        * adaptive Markov chain Monte Carlo (sampling)
        * variational Bayes (clustering)
        * population Monte Carlo (clustering)
        
Platform: Unix
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)
Classifier: Operating System :: Unix
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
