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 danfm.ca/emcee.
Attribution
Please cite Foreman-Mackey, Hogg, Lang & Goodman (2012) if you find this code useful in your research.
License
emcee is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License version 2 as published by the Free Software Foundation.
emcee is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with emcee. If not, see [http://www.gnu.org/licenses/](http://www.gnu.org/licenses/).
Changelog
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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