emcee¶
emcee is an MIT licensed pure-Python implementation of Goodman & Weare’s Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler and these pages will show you how to use it.
This documentation won’t teach you too much about MCMC but there are a lot of resources available for that (try this one). We also published a paper explaining the emcee algorithm and implementation in detail.
emcee has been used in quite a few projects in the astrophysical literature and it is being actively developed on GitHub.
Basic Usage¶
If you wanted to draw samples from a 5 dimensional Gaussian, you would do something like:
import numpy as np
import emcee
def log_prob(x, ivar):
return -0.5 * np.sum(ivar * x ** 2)
ndim, nwalkers = 5, 100
ivar = 1. / np.random.rand(ndim)
p0 = np.random.randn(nwalkers, ndim)
sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob, args=[ivar])
sampler.run_mcmc(p0, 10000)
A more complete example is available in the Quickstart tutorial.
How to Use This Guide¶
To start, you’re probably going to need to follow the Installation guide to get emcee installed on your computer. After you finish that, you can probably learn most of what you need from the from the tutorials listed below (you might want to start with Quickstart and go form there). If you need more details about specific functionality, the User Guide below should have what you need. If you run into any issues, please don’t hesitate to open an issue on GitHub.
License & Attribution¶
Copyright 2010-2019 Dan Foreman-Mackey and contributors.
emcee is free software made available under the MIT License. For details
see the LICENSE
.
If you make use of emcee in your work, please cite our paper (arXiv, ADS, BibTeX) and consider adding your paper to the Testimonials list.
Changelog¶
3.0.0 (upcoming)¶
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
withMPI
.Added
flatlnprobability
property to theEnsembleSampler
object to be consistent with theflatchain
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
inEnsembleSampler
.
1.0.0 (2012-02-15)¶
Initial release.