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 57 articles )

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  1. Aich, Arkajit: Phenomenological dark energy model with hybrid dynamic cosmological constant (2022)
  2. Adshead, Peter; Lozanov, Kaloian D.; Weiner, Zachary J.: Non-Gaussianity and the induced gravitational wave background (2021)
  3. Aviles, Alejandro; Banerjee, Arka; Niz, Gustavo; Slepian, Zachary: Clustering in massive neutrino cosmologies via Eulerian perturbation theory (2021)
  4. Baker, Tessa; Harrison, Ian: Constraining scalar-tensor modified gravity with gravitational waves and large scale structure surveys (2021)
  5. Bergner, Georg; Schaich, David: Eigenvalue spectrum and scaling dimension of lattice (\mathcalN= 4) supersymmetric Yang-Mills (2021)
  6. Byun, Joyce; Oddo, Andrea; Porciani, Cristiano; Sefusatti, Emiliano: Towards cosmological constraints from the compressed modal bispectrum: a robust comparison of real-space bispectrum estimators (2021)
  7. Carrilho, Pedro; Moretti, Chiara; Bose, Benjamin; Markovič, Katarina; Pourtsidou, Alkistis: Interacting dark energy from redshift-space galaxy clustering (2021)
  8. Carter Lee Rhea, Julie Hlavacek-Larrondo, Laurie Rousseau-Nepton, Benjamin Vigneron, Louis-Simon Guité: LUCI: A Python package for SITELLE spectral analysis (2021) arXiv
  9. Leon, Genly; Magaña, Juan; Hernández-Almada, A.; García-Aspeitia, Miguel A.; Verdugo, Tomás; Motta, V.: Barrow entropy cosmology: an observational approach with a hint of stability analysis (2021)
  10. Lepage, G. Peter: Adaptive multidimensional integration: \textscvegasenhanced (2021)
  11. Mairet, Francis; Bayen, Térence: The promise of dawn: microalgae photoacclimation as an optimal control problem of resource allocation (2021)
  12. Makinen, T. Lucas; Charnock, Tom; Alsing, Justin; Wandelt, Benjamin D.: Lossless, scalable implicit likelihood inference for cosmological fields (2021)
  13. Mendonça, I. E. C. R.; Bora, Kamal; Holanda, R. F. L.; Desai, Shantanu: Galaxy clusters, cosmic chronometers and the Einstein equivalence principle (2021)
  14. 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
  15. Oddo, Andrea; Rizzo, Federico; Sefusatti, Emiliano; Porciani, Cristiano; Monaco, Pierluigi: Cosmological parameters from the likelihood analysis of the galaxy power spectrum and bispectrum in real space (2021)
  16. Pordeus-da-Silva, G.; Batista, R. C.; Medeiros, L. G.: Analytical warm dark matter power spectrum on small scales (2021)
  17. Saibaba, Arvind K.; Prasad, Pranjal; de Sturler, Eric; Miller, Eric; Kilmer, Misha E.: Randomized approaches to accelerate MCMC algorithms for Bayesian inverse problems (2021)
  18. Shaposhnikov, Mikhail; Shkerin, Andrey; Timiryasov, Inar; Zell, Sebastian: Higgs inflation in Einstein-Cartan gravity (2021)
  19. Zatrimaylov, K.: On filaments, prolate halos and rotation curves (2021)
  20. Alawieh, Leen; Goodman, Jonathan; Bell, John B.: Iterative construction of Gaussian process surrogate models for Bayesian inference (2020)

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