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emcee is a Python library implementing a class of affine-invariant ensemblesamplers for Markov chain Monte Carlo (MCMC). This package has been widelyapplied to probabilistic modeling problems in astrophysics where it wasoriginally published, with some applications in other fields. When it was firstreleased in 2012, the interface implemented in emcee was fundamentallydifferent from the MCMC libraries that were popular at the time, such as PyMC,because it was specifically designed to work with "black box" models instead ofstructured graphical models. This has been a popular interface for applicationsin astrophysics because it is often non-trivial to implement realistic physicswithin the modeling frameworks required by other libraries. Since emcee'srelease, other libraries have been developed with similar interfaces, such asdynesty (Speagle 2019). The version 3.0 release of emcee is the first majorrelease of the library in about 6 years and it includes a full re-write of thecomputational backend, several commonly requested features, and a set of new"move" implementations.

emcee v3: A Python ensemble sampling toolkit for affine-invariant MCMC