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2019•Australian & New Zealand Journal of StatisticsRequires access

R package rjmcmc: reversible jump MCMC using post‐processing

Nicholas Gelling, Matthew Schofield, Richard Barker

Open publisher page 6 citations

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.

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What this paper is about

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.

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Available 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.

Key concepts: Reversible-jump Markov chain Monte Carlo, Markov chain Monte Carlo, Jump, Bayes' theorem, Mathematics, R package, Algorithm, Markov chain

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