A Python package for Bayesian estimation using Markov Chain Monte Carlo

Strickland, C.M., Denham, R.J., Alston, C.L., & Mengersen, K.L. (2013) A Python package for Bayesian estimation using Markov Chain Monte Carlo. In Alston, Clair L., Mengersen, Kerrie L., & Pettitt, Anthony N. (Eds.) Case Studies in Bayesian Statistical Modelling and Analysis. John Wiley and Sons, Chichester, West Sussex, pp. 421-460.

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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 specific 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: Book Chapter
Keywords: MCMC, Metropolis Hastings, Gibbs, Bayesian, OBMC, slice sampler, Python
DOI: 10.1002/9781118394472.ch25
ISBN: 9781119941828
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 John Wiley and Sons
Deposited On: 21 Jul 2011 05:47
Last Modified: 14 Jul 2017 00:19

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