%0 Journal Article %1 foreman-mackey_emcee:_2012 %A Foreman-Mackey, Daniel %A Hogg, David %A Lang, Dustin %A Goodman, Jonathan %D 2012 %K Bayesian Carlo, Markov Monte Python, Sampler adaptive, chain ensemble, inference, nuissance, %T emcee: The MCMC Hammer %U http://arxiv.org/pdf/1202.3665.pdf %X We introduce 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. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to \$\textbackslashsim Nˆ2\$ for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation and API. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at this http URL under the GNU General Public License v2.

@article{foreman-mackey_emcee:_2012, abstract = {We introduce 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. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to \${\textbackslash}sim N{\textasciicircum}2\$ for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation and API. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at this http URL under the GNU General Public License v2.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Foreman-Mackey, Daniel and Hogg, David and Lang, Dustin and Goodman, Jonathan}, biburl = {https://www.bibsonomy.org/bibtex/267feecf700fe58194345ad516ee1f24b/yourwelcome}, interhash = {4bb4486c47144472df8eacdd03b17ea5}, intrahash = {67feecf700fe58194345ad516ee1f24b}, keywords = {Bayesian Carlo, Markov Monte Python, Sampler adaptive, chain ensemble, inference, nuissance,}, shorttitle = {emcee}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {emcee: {The} {MCMC} {Hammer}}, url = {http://arxiv.org/pdf/1202.3665.pdf}, urldate = {2012-02-18}, year = 2012 }