MC3: Multi-Core Markov-Chain Monte Carlo

Build Status Documentation Status Latest Version License


Author:Patricio Cubillos and collaborators (see Collaborators)
Contact:patricio.cubillos[at]oeaw.ac.at
Organizations:Space Research Institute (IWF)
Web Site:https://github.com/pcubillos/mc3
Date:Aug 10, 2019

Note

MC3 got an extreme make over! (version 3.0) and now follows the current best practices for Python development. The package changed name from MCcubed to mc3, it is now pip-installable (pip install mc3), it added support for nested sampling, and is extensively tested with pytest and travis.

Features

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).
  • Nested-sampling via dynesty.

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.

Note

MC3 works in both Python2.7 and Python3.6+. However, support for Python2 will end on Jan 1, 2020.

Collaborators

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]oeaw.ac.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!