Multi-Core Markov-Chain Monte Carlo (MC3)¶
| Author: | Patricio Cubillos and collaborators (see Team Members) |
|---|---|
| Contact: | patricio.cubillos[at]oeaw.ac.at |
| Organizations: | University of Central Florida (UCF), Space Research Institute (IWF) |
| Web Site: | https://github.com/pcubillos/MCcubed |
| Date: | Aug 10, 2019 |
Features¶
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¶
- Patricio Cubillos (UCF, IWF) patricio.cubillos[at]oeaw.ac.at
- Joseph Harrington (UCF)
- Nate Lust (UCF)
- AJ Foster (UCF)
- Madison Stemm (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, but do not necessarily guarantee support. Please send feedback or inquiries to:
Patricio Cubillos (patricio.cubillos[at]oeaw.ac.at)
Thank you for using MC3!