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

TL;DR

emcee v3 is a major update to the Python ensemble sampling toolkit for affine-invariant MCMC, featuring a complete backend rewrite, new features, and additional move algorithms, enhancing its usability for probabilistic modeling in astrophysics and beyond.

Contribution

The paper introduces emcee v3, a significantly improved version with a full backend rewrite, new features, and additional move implementations, expanding its capabilities and performance.

Findings

Complete backend overhaul improves performance

New move algorithms enhance sampling efficiency

Extended features support broader modeling applications

Abstract

emcee is a Python library implementing a class of affine-invariant ensemble samplers for Markov chain Monte Carlo (MCMC). This package has been widely applied to probabilistic modeling problems in astrophysics where it was originally published, with some applications in other fields. When it was first released in 2012, the interface implemented in emcee was fundamentally different 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 of structured graphical models. This has been a popular interface for applications in astrophysics because it is often non-trivial to implement realistic physics within the modeling frameworks required by other libraries. Since emcee's release, other libraries have been developed with similar interfaces, such as dynesty (Speagle 2019). The version 3.0 release of emcee is the first major release of the library in about 6 years and it includes a full re-write of the computational backend, several commonly requested features, and a set of new "move" implementations.