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PyMCMC : a Python package for Bayesian Estimation using Markov chain Monte Carlo

Strickland, C. M., Denham, R. J., Alston, C. L., & Mengersen, K. L. (2011) PyMCMC : a Python package for Bayesian Estimation using Markov chain Monte Carlo. [Working Paper] (Submitted (not yet accepted for publication))

Abstract

Markov chain Monte Carlo (MCMC) estimation provides a solution to the complex integration problems that are faced in the Bayesian analysis of statistical problems. The implementation of MCMC algorithms is, however, code intensive and time consuming. We have developed a Python package, which is called PyMCMC, that aids in the construction of MCMC samplers and helps to substantially reduce the likelihood of coding error, as well as aid in the minimisation of repetitive code. PyMCMC contains classes for Gibbs, Metropolis Hastings, independent Metropolis Hastings, random walk Metropolis Hastings, orientational bias Monte Carlo and slice samplers as well as speci�c modules for common models such as a module for Bayesian regression analysis. PyMCMC is straightforward to optimise, taking advantage of the Python libraries Numpy and Scipy, as well as being readily extensible with C or Fortran.

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ID Code: 43469
Item Type: Working Paper
Keywords: MCMC, Metropolis Hastings, Gibbs, Bayesian, OBMC, slice sampler, Python
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2011 the authors.
Deposited On: 21 Jul 2011 15:47
Last Modified: 07 Sep 2013 02:01

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