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))
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.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|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 05:47|
|Last Modified:||06 Sep 2013 16:01|
Repository Staff Only: item control page