Multi-Core Markov-Chain Monte Carlo (MC3)

Author:Patricio Cubillos and collaborators (see Team Members)
Organizations:University of Central Florida (UCF), Space Research Institute (IWF)
Web Site:
Date:Aug 10, 2019


MC3 is a powerful 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, or
    • Differential-Evolution MCMC (recomended).

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.

Team Members


MC3 is open-source open-development software under the MIT License.

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, but do not necessarily guarantee support. Please send feedback or inquiries to:

Thank you for using MC3!

Documentation for Previous Releases