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Paul Miles edited this page Jul 23, 2019 · 11 revisions

pymcmcstat

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The pymcmcstat package is a Python program for running Markov Chain Monte Carlo (MCMC) simulations. Details regarding the underlying theory for parameter estimation used within pymcmcstat can be found on the Program Methodology page. Included in this package is the ability to use different Metropolis based sampling techniques:

  • Metropolis-Hastings (MH): Primary sampling method.
  • Adaptive-Metropolis (AM): Adapts covariance matrix at specified intervals.
  • Delayed-Rejection (DR): Delays rejection by sampling from a narrower distribution. Capable of n-stage delayed rejection.
  • Delayed Rejection Adaptive Metropolis (DRAM): DR + AM

This package is an adaptation of the MATLAB toolbox mcmcstat. The user interface is designed to be as similar to the MATLAB version as possible, but this implementation has taken advantage of certain data structure concepts more amenable to Python.

Note, advanced plotting routines are available in the mcmcplot package. Many plotting features are directly available within pymcmcstat, but some user's may find mcmcplot useful.

Installation

This code can be found on the Github project page. This package is available on the PyPI distribution site and the latest version can be installed via

pip install pymcmcstat

pypi

The master branch on Github typically matches the latest version on the PyPI distribution site. To install the master branch directly from Github,

pip install git+https://github.com/prmiles/pymcmcstat.git

You can also clone the repository and run python setup.py install.

Getting Started

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License

MIT

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Contributors

See the GitHub contributor page

Citing pymcmcstat

Miles, (2019). pymcmcstat: A Python Package for Bayesian Inference Using Delayed Rejection Adaptive Metropolis. Journal of Open Source Software, 4(38), 1417, https://doi.org/10.21105/joss.01417

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Also, be sure to cite the version of the code you are using by referencing the appropriate Zenodo archive.  Follow the DOI badge to the Zenodo site or see if the link is available within the release history.

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Sponsor

This work was sponsored in part by the NNSA Office of Defense Nuclear Nonproliferation R&D through the Consortium for Nonproliferation Enabling Capabilities.

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