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https://github.com/dfm/emcee

The Python ensemble sampling toolkit for affine-invariant MCMC
https://github.com/dfm/emcee

mcmc mcmc-sampler probabilistic-data-analysis python

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The Python ensemble sampling toolkit for affine-invariant MCMC

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emcee
=====

**The Python ensemble sampling toolkit for affine-invariant MCMC**

.. image:: https://img.shields.io/badge/GitHub-dfm%2Femcee-blue.svg?style=flat
:target: https://github.com/dfm/emcee
.. image:: https://github.com/dfm/emcee/workflows/Tests/badge.svg
:target: https://github.com/dfm/emcee/actions?query=workflow%3ATests
.. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat
:target: https://github.com/dfm/emcee/blob/main/LICENSE
.. image:: http://img.shields.io/badge/arXiv-1202.3665-orange.svg?style=flat
:target: https://arxiv.org/abs/1202.3665
.. image:: https://coveralls.io/repos/github/dfm/emcee/badge.svg?branch=main&style=flat&v=2
:target: https://coveralls.io/github/dfm/emcee?branch=main
.. image:: https://readthedocs.org/projects/emcee/badge/?version=latest
:target: http://emcee.readthedocs.io/en/latest/?badge=latest

emcee is a stable, well tested Python implementation of the affine-invariant
ensemble sampler for Markov chain Monte Carlo (MCMC)
proposed by
`Goodman & Weare (2010) `_.
The code is open source and has
already been used in several published projects in the Astrophysics
literature.

Documentation
-------------

Read the docs at `emcee.readthedocs.io `_.

Attribution
-----------

Please cite `Foreman-Mackey, Hogg, Lang & Goodman (2012)
`_ if you find this code useful in your
research. The BibTeX entry for the paper is::

@article{emcee,
author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.},
title = {emcee: The MCMC Hammer},
journal = {PASP},
year = 2013,
volume = 125,
pages = {306-312},
eprint = {1202.3665},
doi = {10.1086/670067}
}

License
-------

Copyright 2010-2021 Dan Foreman-Mackey and contributors.

emcee is free software made available under the MIT License. For details see
the LICENSE file.