Abstract: emcee is an extensible, pure-Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. It's designed for Bayesian parameter estimation. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to $sim N^2$ for a traditional algorithm in an N-dimensional parameter space. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort.
Credit: Foreman-Mackey, Daniel; Conley, Alex; Meierjurgen Farr, Will; Hogg, David W.; Lang, Dustin; Marshall, Phil; Price-Whelan, Adrian; Sanders, Jeremy; Zuntz, Joe
Preferred citation method: http://adsabs.harvard.edu/abs/2013PASP..125..306F
Reason: Updated code entry.