mc3: Multi-Core Markov-Chain Monte Carlo

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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:

Feb 27, 2025

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Note

Got Python3.9+? Simply install as: pip install mc3

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).

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.

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)

Documentation

• Getting Started
  • System Requirements
  • Install
  • Example 1: Interactive Run
  • Example 2: Shell Run
• MCMC Tutorial
  • Preamble
  • Argument Inputs
  • Input Data
  • Modeling Function
  • Fitting Parameters
  • Parameters Stepping Behavior
    • Stepping Behavior
    • Stepping Scale
  • Parameter Priors
  • Parameter Names
  • Sampler Algorithm
  • MCMC Configuration
    • Pre-MCMC Setup
  • Optimization
  • Convergence
  • Wavelet-Likelihood MCMC
  • Fine-tuning
  • Logging
  • Outputs
  • MCMC Run
  • Resume a Previous Run
  • Inputs from Files
    • Input Data File
    • Model Parameters
• Optimization Tutorial
  • Basic Fit
  • Data and Uncertainties
  • Modeling Function
  • Optimization Algorithm
  • Fixing and Sharing Paramerers
  • Parameter Priors
• Time Averaging
  • Example
• References
• API
  • mc3
    • sample()
    • fit()
  • mc3.plots
    • rms()
    • trace()
    • modelfit()
    • histogram()
    • pairwise()
    • subplotter()
    • subplot()
    • _histogram()
    • _pairwise()
    • hist_2D()
    • Marginal
    • Figure
    • Posterior
    • alphatize()
    • rainbow_text()
    • Theme
    • THEMES
  • mc3.utils
    • ROOT
    • parray()
    • saveascii()
    • loadascii()
    • savebin()
    • loadbin()
    • isfile()
    • burn()
    • default_parnames()
    • tex_parameters()
    • Log
  • mc3.stats
    • gelman_rubin()
    • bin_array()
    • residuals()
    • chisq()
    • dwt_chisq()
    • log_prior()
    • cred_region()
    • ppf_uniform
    • ppf_gaussian
    • dwt_daub4()
    • Loglike
    • Prior_transform
    • marginal_statistics()
    • update_output()
    • calc_bestfit_statistics()
    • calc_sample_statistics()
    • summary_stats()
    • time_avg()
    • prayer_beads()
• Contributing
  • Raising Issues
  • Programming Style
  • Pull Requests
• 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 or inquiries, please refer them to:

Patricio Cubillos (patricio.cubillos[at]oeaw.ac.at)

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