R package rjmcmc: reversible jump MCMC using post‐processing

Nicholas Gelling; Matthew R. Schofield; Richard J. Barker

doi:10.1111/anzs.12263

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R package rjmcmc: reversible jump MCMC using post‐processing

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R package rjmcmc: reversible jump MCMC using post‐processing

Nicholas Gelling, Matthew R. Schofield and Richard J. Barker

Australian & New Zealand journal of statistics, Vol.61(2), pp.189-212

06/2019

DOI: https://doi.org/10.1111/anzs.12263

Abstract

automatic differentiation

Bayes factors

Bayesian multimodel inference

Markov chain Monte Carlo

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. The rjmcmc package uses a reversible jump algorithm to estimate posterior model probabilities, by post‐processing MCMC output from each model.

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Record Identifier: 9926515124101891
Title: R package rjmcmc: reversible jump MCMC using post‐processing
Creators: Nicholas Gelling
Matthew R. Schofield
Richard J. Barker
Publication Details: Australian & New Zealand journal of statistics, Vol.61(2), pp.189-212
Academic Unit: Sciences Divisional Office; Mathematics and Statistics
Date published ; e-published: 06/2019
Language: English
Resource Type: Journal article

R package rjmcmc: reversible jump MCMC using post‐processing

Abstract

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