emcee

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 32 articles )

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  1. Mairet, Francis; Bayen, Térence: The promise of dawn: microalgae photoacclimation as an optimal control problem of resource allocation (2021)
  2. Nightingale, J. W., Hayes, R., Kelly, A., Amvrosiadis, A., Etherington, A., He, Q., Li, N., Cao, X., Frawley, J., Cole, S., Enia, A., Frenk, C., Harvey, D., Li, R., Massey, R., Negrello, M., Robertson, A: PyAutoLens: Open-Source Strong Gravitational Lensing (2021) not zbMATH
  3. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)
  4. Andrew R. McCluskey; Tim Snow: uravu: Making Bayesian modelling easy(er) (2020) not zbMATH
  5. Carpio, Ana; Iakunin, Sergei; Stadler, Georg: Bayesian approach to inverse scattering with topological priors (2020)
  6. Dinner, Aaron R.; Thiede, Erik H.; Koten, Brian Van; Weare, Jonathan: Stratification as a general variance reduction method for Markov chain Monte Carlo (2020)
  7. Dufays, Arnaud; Rombouts, Jeroen V. K.: Relevant parameter changes in structural break models (2020)
  8. G. Peter Lepage: Adaptive Multidimensional Integration: VEGAS Enhanced (2020) arXiv
  9. Martin Nielsen, Guy Davies, Oliver Hall, et al.: PBjam: A Python package for automating asteroseismology of solar-like oscillators (2020) arXiv
  10. Argüelles, C. A.; Schneider, A.; Yuan, T.: A binned likelihood for stochastic models (2019)
  11. 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
  12. 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
  13. Eric W. Koch, Ryan D. Boyden, Blakesley Burkhart, Adam Ginsburg, Jason L. Loeppky, Stella S.R. Offner: TurbuStat: Turbulence Statistics in Python (2019) arXiv
  14. Joshua S Speagle: dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences (2019) arXiv
  15. Kumar, R.; Colin, C.; Hartikainen, A.; Martin, O. A.: ArviZ a unified library for exploratory analysis of Bayesian models in Python. (2019) not zbMATH
  16. Morzfeld, M.; Tong, X. T.; Marzouk, Y. M.: Localization for MCMC: sampling high-dimensional posterior distributions with local structure (2019)
  17. 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
  18. 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
  19. Benjamin J. Fulton; Erik A. Petigura; Sarah Blunt; Evan Sinukoff: RadVel: The Radial Velocity Modeling Toolkit (2018) arXiv
  20. Brendon Brewer; Daniel Foreman-Mackey: DNest4: Diffusive Nested Sampling in C++ and Python (2018) not zbMATH

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