Kick ass affine-invariant ensemble MCMC sampling
Project description
The Python ensemble sampling toolkit for affine-invariant MCMC
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 dan.iel.fm/emcee.
Attribution
Please cite Foreman-Mackey, Hogg, Lang & Goodman (2012) if you find this code useful in your research and add your paper to the testimonials list. 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 2010-2016 Dan Foreman-Mackey and contributors.
emcee is free software made available under the MIT License. For details see the LICENSE file.
Changelog
2.2.0 (2016-07-12)
Improved autocorrelation time computation.
Numpy compatibility issues.
Fixed deprecated integer division behavior in PTSampler.
2.1.0 (2014-05-22)
Removing dependence on acor extension.
Added arguments to PTSampler function.
Added automatic load-balancing for MPI runs.
Added custom load-balancing for MPI and multiprocessing.
New default multiprocessing pool that supports ^C.
2.0.0 (2013-11-17)
Re-licensed under the MIT license!
Clearer less verbose documentation.
Added checks for parameters becoming infinite or NaN.
Added checks for log-probability becoming NaN.
Improved parallelization and various other tweaks in PTSampler.
1.2.0 (2013-01-30)
Added a parallel tempering sampler PTSampler.
Added instructions and utilities for using emcee with MPI.
Added flatlnprobability property to the EnsembleSampler object to be consistent with the flatchain property.
Updated document for publication in PASP.
Various bug fixes.
1.1.3 (2012-11-22)
Made the packaging system more robust even when numpy is not installed.
1.1.2 (2012-08-06)
Another bug fix related to metadata blobs: the shape of the final blobs object was incorrect and all of the entries would generally be identical because we needed to copy the list that was appended at each step. Thanks goes to Jacqueline Chen (MIT) for catching this problem.
1.1.1 (2012-07-30)
Fixed bug related to metadata blobs. The sample function was yielding the blobs object even when it wasn’t expected.
1.1.0 (2012-07-28)
Allow the lnprobfn to return arbitrary “blobs” of data as well as the log-probability.
Python 3 compatible (thanks Alex Conley)!
Various speed ups and clean ups in the core code base.
New documentation with better examples and more discussion.
1.0.1 (2012-03-31)
Fixed transpose bug in the usage of acor in EnsembleSampler.
1.0.0 (2012-02-15)
Initial release.
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