mc3: Multi-Core Markov-Chain Monte Carlo

Build Status Documentation Status Latest Version conda 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:Apr 19, 2021

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 is compatible with Python3.6+. (There is support for Python2.7 up to mc3 version 3.0.1).

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!