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version](https://badge.fury.io/py/pypmc.svg)](https://badge.fury.io/py/pypmc)\n[![Conda Forge version](https://anaconda.org/conda-forge/pypmc/badges/version.svg)](https://anaconda.org/conda-forge/pypmc)\n[![DOI](https://zenodo.org/badge/15123/fredRos/pypmc.svg)](https://zenodo.org/badge/latestdoi/15123/fredRos/pypmc)\n[![Build/Check/Deploy to PyPI](https://github.com/pypmc/pypmc/actions/workflows/manylinx-build+check+deploy.yaml/badge.svg)](https://github.com/pypmc/pypmc/actions/workflows/manylinx-build+check+deploy.yaml)\n\npypmc\n=====\n\n``pypmc`` is a python package focusing on adaptive importance\nsampling. It can be used for integration and sampling from a\nuser-defined target density. A typical application is Bayesian\ninference, where one wants to sample from the posterior to marginalize\nover parameters and to compute the evidence. The key idea is to create\na good proposal density by adapting a mixture of Gaussian or student's\nt components to the target density. The package is able to efficiently\nintegrate multimodal functions in up to about 30-40 dimensions at the\nlevel of 1% accuracy or less. 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Feel\nfree to save and modify them according to your needs.\n\nDocumentation\n-------------\n\nThe full documentation with a manual and api description is available at\n[here](http://pypmc.github.io/).\n\nCredits\n-------\n\npypmc was developed by Stephan Jahn (TU Munich) under the supervision\nof Frederik Beaujean (LMU Munich) as part of Stephan's master's thesis\nat the Excellence Cluster Universe, Garching, Germany, in 2014.\n\nIf you use ``pypmc`` in academic work, we kindly ask you to cite the\nrespective release as indicated by the zenodo DOI above. Thanks!\n\nDay to day maintenance is assisted by Danny van Dyk.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpypmc%2Fpypmc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpypmc%2Fpypmc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpypmc%2Fpypmc/lists"}