{ "info": { "author": "Joel Akeret", "author_email": "jakeret@phys.ethz.ch", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", "Natural Language :: English", "Operating System :: MacOS", "Operating System :: POSIX", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Astronomy", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Scientific/Engineering :: Physics" ], "description": "=============================\nabcpmc\n=============================\n\n.. image:: https://badge.fury.io/py/abcpmc.svg\n :target: http://badge.fury.io/py/abcpmc\n\n.. image:: https://travis-ci.org/jakeret/abcpmc.svg?branch=master\n :target: https://travis-ci.org/jakeret/abcpmc\n \n.. image:: https://coveralls.io/repos/jakeret/abcpmc/badge.svg?branch=master\n :target: https://coveralls.io/r/jakeret/abcpmc?branch=master\n\n.. image:: https://img.shields.io/badge/docs-latest-blue.svg?style=flat\n :target: http://abcpmc.readthedocs.org/en/latest\n\n.. image:: http://img.shields.io/badge/arXiv-1504.07245-orange.svg?style=flat\n :target: http://arxiv.org/abs/1504.07245\n\n\n\nA Python Approximate Bayesian Computing (ABC) Population Monte Carlo (PMC) implementation based on Sequential Monte Carlo (SMC) with Particle Filtering techniques.\n\n.. image:: https://raw.githubusercontent.com/jakeret/abcpmc/master/docs/abcpmc.png\n :alt: approximated 2d posterior (created with triangle.py).\n :align: center\n\nThe **abcpmc** package has been developed at ETH Zurich in the `Software Lab of the Cosmology Research Group `_ of the `ETH Institute of Astronomy `_. \n\nThe development is coordinated on `GitHub `_ and contributions are welcome. 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