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 23 articles )

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  1. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)
  2. Argüelles, C. A.; Schneider, A.; Yuan, T.: A binned likelihood for stochastic models (2019)
  3. Clerx, M., Robinson, M., Lambert, B., Lei, C.L., Ghosh, S., Mirams, G.R. and Gavaghan, D.J.: Probabilistic Inference on Noisy Time Series (PINTS) (2019) not zbMATH
  4. D. Huppenkothen, M. Bachetti, A. L. Stevens, S. Migliari, P. Balm, O. Hammad, U. M. Khan, H. Mishra, H. Rashid, S. Sharma, R. V. Blanco, E. M. Ribeiro: Stingray: A Modern Python Library For Spectral Timing (2019) arXiv
  5. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  6. Joshua S Speagle: dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences (2019) arXiv
  7. Kumar, R.; Colin, C.; Hartikainen, A.; Martin, O. A.: ArviZ a unified library for exploratory analysis of Bayesian models in Python. (2019) not zbMATH
  8. P. Mollière, J.P. Wardenier, R. van Boekel, Th. Henning, K. Molaverdikhani, I. A. G. Snellen: petitRADTRANS: a Python radiative transfer package for exoplanet characterization and retrieval (2019) arXiv
  9. Sarah Blunt, Jason Wang, Isabel Angelo, Henry Ngo, Devin Cody, Robert J. De Rosa, James Graham, Lea Hirsch, Vighnesh Nagpal, Eric L. Nielsen, Logan Pearce, Malena Rice, Roberto Tejada: orbitize!: A Comprehensive Orbit-fitting Software Package for the High-contrast Imaging Community (2019) arXiv
  10. Benjamin J. Fulton; Erik A. Petigura; Sarah Blunt; Evan Sinukoff: RadVel: The Radial Velocity Modeling Toolkit (2018) arXiv
  11. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH
  12. C. M. Biwer; Collin D. Capano; Soumi De; Miriam Cabero; Duncan A. Brown; Alexander H. Nitz; V. Raymond: PyCBC Inference: A Python-based parameter estimation toolkit for compact binary coalescence signals (2018) arXiv
  13. D.M. Straub: flavio: a Python package for flavour and precision phenomenology in the Standard Model and beyond (2018) arXiv
  14. Leimkuhler, Benedict; Matthews, Charles; Weare, Jonathan: Ensemble preconditioning for Markov chain Monte Carlo simulation (2018)
  15. Morzfeld, Matthias; Day, Marcus S.; Grout, Ray W.; Heng Pau, George Shu; Finsterle, Stefan A.; Bell, John B.: Iterative importance sampling algorithms for parameter estimation (2018)
  16. Schreiber, Jacob: pomegranate: fast and flexible probabilistic modeling in Python (2018)
  17. Zhang, Jiaxin; Shields, Michael D.: The effect of prior probabilities on quantification and propagation of imprecise probabilities resulting from small datasets (2018)
  18. Guillochon, J.; Nicholl, M.; Villar, VA; Mockler, B.; Narayan, G.; Mandel, KS; Berger, E.; Williams, PKG: MOSFiT: Modular Open-Source Fitter for Transients (2017) arXiv
  19. Hilbe, Joseph M.; de Souza, Rafael S.; Ishida, Emille E. O.: Bayesian models for astrophysical data. Using R, JAGS, Python, and Stan (2017)
  20. Kandasamy, Kirthevasan; Schneider, Jeff; Póczos, Barnabás: Query efficient posterior estimation in scientific experiments via Bayesian active learning (2017)

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