emcee: The MCMC Hammer. 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 ∼N2 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 http://dan.iel.fm/emcee/current/ under the MIT License.

References in zbMATH (referenced in 34 articles )

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  3. Mairet, Francis; Bayen, Térence: The promise of dawn: microalgae photoacclimation as an optimal control problem of resource allocation (2021)
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  7. Carpio, Ana; Iakunin, Sergei; Stadler, Georg: Bayesian approach to inverse scattering with topological priors (2020)
  8. Dinner, Aaron R.; Thiede, Erik H.; Koten, Brian Van; Weare, Jonathan: Stratification as a general variance reduction method for Markov chain Monte Carlo (2020)
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  10. Ekaterina Ilin: AltaiPony - Flare science in Kepler, K2 and TESS light curves (2020) not zbMATH
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  16. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  17. Joshua S Speagle: dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences (2019) arXiv
  18. Kumar, R.; Colin, C.; Hartikainen, A.; Martin, O. A.: ArviZ a unified library for exploratory analysis of Bayesian models in Python. (2019) not zbMATH
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