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

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arxiv 1911.07688 v1 pith:BOPATMVF submitted 2019-11-18 astro-ph.IM stat.CO

Daniel Foreman-Mackey , Will M. Farr , Manodeep Sinha , Anne M. Archibald , David W. Hogg , Jeremy S. Sanders , Joe Zuntz , Peter K. G. Williams
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Andrew R. J. Nelson Miguel de Val-Borro Tobias Erhardt Ilya Pashchenko Oriol Abril Pla
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classification astro-ph.IMstat.CO
keywords emceebeenlibrariesmcmcotherreleaseaffine-invariantapplications
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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.

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Cited by 2 Pith papers

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  1. Dark energy era with a resolution of Hubble tension in generalized entropic cosmology

    gr-qc 2025-07 reject novelty 4.0 of 10

    A generalized-entropy dark energy model with one fitted extra parameter returns H0 near 73 km/s/Mpc on some datasets, but the reported model-comparison statistics do not favor it over LambdaCDM.

  2. Exploring parametrized dark energy models in interacting scenario

    gr-qc 2025-05 reject novelty 4.0 of 10

    An interacting dark energy model with a linear equation-of-state parametrization is fitted to low-redshift data, yielding a fitted H0 of 75.6 for one dataset, but the model's H(z) does not satisfy its own Friedmann eq...

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