A Python package for Bayesian estimation using Markov Chain Monte Carlo


Autoria(s): Strickland, C.M.; Denham, R.J.; Alston, C.L.; Mengersen, K.L.
Contribuinte(s)

Alston, Clair L.

Mengersen, Kerrie L.

Pettitt, Anthony N.

Data(s)

2013

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/43469/

Publicador

John Wiley and Sons

Relação

http://eprints.qut.edu.au/43469/4/43469.pdf

DOI:10.1002/9781118394472.ch25

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.

http://purl.org/au-research/grants/ARC/LP100100565

Direitos

Copyright 2013 John Wiley and Sons

Fonte

Faculty of Science and Technology; Science & Engineering Faculty; Mathematical Sciences

Palavras-Chave #MCMC, Metropolis Hastings, Gibbs, Bayesian, OBMC, slice sampler, Python
Tipo

Book Chapter