The pymcmcstat package is a Python program for running Markov Chain Monte Carlo (MCMC) simulations. 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.
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
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.
Please see the pymcmcstat homepage or follow the DOI badge above to find the appropriate citation information.
This work was sponsored in part by the NNSA Office of Defense Nuclear Nonproliferation R&D through the Consortium for Nonproliferation Enabling Capabilities.
- pymcmcstat package
- pymcmcstat.chain package
- pymcmcstat.plotting package
- pymcmcstat.procedures package
- pymcmcstat.samplers package
- pymcmcstat.settings package
- pymcmcstat.structures package
- pymcmcstat.utilities package
|[BG98]||Stephen P Brooks and Andrew Gelman. General methods for monitoring convergence of iterative simulations. Journal of computational and graphical statistics, 7(4):434–455, 1998.|
|[BR98]||Stephen P Brooks and Gareth O Roberts. Assessing convergence of markov chain monte carlo algorithms. Statistics and Computing, 8(4):319–335, 1998. URL: http://www.math.pitt.edu/~swigon/Homework/brooks97assessing.pdf.|
|[GR+92]||Andrew Gelman, Donald B Rubin, and others. Inference from iterative simulation using multiple sequences. Statistical science, 7(4):457–472, 1992.|
|[HLMS06]||Heikki Haario, Marko Laine, Antonietta Mira, and Eero Saksman. Dram: efficient adaptive mcmc. Statistics and Computing, 16(4):339–354, 2006. URL: https://link.springer.com/article/10.1007/s11222-006-9438-0.|
|[HST+01]||Heikki Haario, Eero Saksman, Johanna Tamminen, and others. An adaptive metropolis algorithm. Bernoulli, 7(2):223–242, 2001. URL: https://projecteuclid.org/euclid.bj/1080222083.|
|[MT00]||George Marsaglia and Wai Wan Tsang. A simple method for generating gamma variables. ACM Transactions on Mathematical Software (TOMS), 26(3):363–372, 2000. URL: https://dl.acm.org/citation.cfm?id=358414.|
|[Smi14]||Ralph C. Smith. Uncertainty quantification: theory, implementation, and applications. Volume 12. SIAM, 2014.|