The Python ensemble sampling toolkit for affine-invariant MCMC
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 emcee.readthedocs.io.
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 2010-2017 Dan Foreman-Mackey and contributors.
emcee is free software made available under the MIT License. For details see the LICENSE file.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for emcee-3.0rc2-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1e9102c1df3c68a9bb119c0482b2bab7b172646b6f90d4d3c7447b1e992dfa64 |
|
MD5 | 9d728ca0366ad9560d89b1d796f6dc39 |
|
BLAKE2b-256 | 88bd914abc01c619d83892fea74a4e02213272ba912ffca787c9f149f92f1339 |