mc3: Multi-Core Markov-Chain Monte Carlo

Build Status Documentation Status Latest Version conda License

Author:Patricio Cubillos and collaborators (see Collaborators)
Organizations:Space Research Institute (IWF)
Web Site:
Date:Jun 24, 2023


Got Python3.6+? Simply install as: pip install mc3


mc3 is a Bayesian-statistics tool that offers:

  • Levenberg-Marquardt least-squares optimization.
  • Markov-chain Monte Carlo (MCMC) posterior-distribution sampling following the:
    • Metropolis-Hastings algorithm with Gaussian proposal distribution,
    • Differential-Evolution MCMC (DEMC), or
    • DEMCzs (Snooker).

The following features are available when running mc3:

  • Execution from the Shell prompt or interactively through the Python interpreter.
  • Single- or multiple-CPU parallel computing.
  • Uniform non-informative, Jeffreys non-informative, or Gaussian-informative priors.
  • Gelman-Rubin convergence test.
  • Share the same value among multiple parameters.
  • Fix the value of parameters to constant values.
  • Correlated-noise estimation with the Time-averaging or the Wavelet-based Likelihood estimation methods.


All of these people have made a direct or indirect contribution to mc3, and in many instances have been fundamental in the development of this package.

  • Patricio Cubillos (UCF, IWF) patricio.cubillos[at]
  • Joseph Harrington (UCF)
  • Nate Lust (UCF)
  • AJ Foster (UCF)
  • Madison Stemm (UCF)
  • Tom Loredo (Cornell)
  • Kevin Stevenson (UCF)
  • Chris Campo (UCF)
  • Matt Hardin (UCF)
  • Ryan Hardy (UCF)
  • Monika Lendl (IWF)
  • Ryan Challener (UCF)
  • Michael Himes (UCF)

Be Kind

Please cite this paper if you found mc3 useful for your research:
Cubillos et al. (2017): On the Correlated-noise Analyses Applied to Exoplanet Light Curves, AJ, 153, 3.

We welcome your feedback or inquiries, please refer them to:

mc3 is open-source open-development software under the MIT License.
Thank you for using mc3!