R package rjmcmc: reversible jump MCMC using post‐processing. (5th July 2019)

• Record Type: Journal Article
• Authors: Gelling, Nicholas; Schofield, Matthew R.; Barker, Richard J.
• Is Part Of: Australian & New Zealand journal of statistics. Volume 61:Number 2(2019)
• Page Start: 189
• Page End: 212
• Publication Date: 2019-07-05
• Subjects: automatic differentiation -- Bayesian multimodel inference -- Bayes factors -- Markov chain Monte Carlo; Statistics -- Periodicals; 519.5
• DOI: 10.1111/anzs.12263
• Languages: English
• ISSNs: 1369-1473

Abstract

Summary: The rjmcmc package for R implements the post‐processing reversible jump Markov chain Monte Carlo (MCMC) algorithm of Barker & Link. MCMC output from each of the models is used to estimate posterior model probabilities and Bayes factors. Automatic differentiation is used to simplify implementation. The package is demonstrated on two examples. Abstract : The rjmcmc package uses a reversible jump algorithm to estimate posterior model probabilities, by post‐processing MCMC output from each model.