Original Article
R package rjmcmc: reversible jump MCMC using post-processing
Nicholas Gelling,
Matthew R. Schofield,
Richard J. Barker,
Corresponding Author
Nicholas Gelling
Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin , 9016 New Zealand
Author to whom correspondence should be addressed.Search for more papers by this authorMatthew R. Schofield
Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin , 9016 New Zealand
Search for more papers by this authorRichard J. Barker
Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin , 9016 New Zealand
Search for more papers by this authorNicholas Gelling,
Matthew R. Schofield,
Richard J. Barker,
Corresponding Author
Nicholas Gelling
Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin , 9016 New Zealand
Author to whom correspondence should be addressed.Search for more papers by this authorMatthew R. Schofield
Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin , 9016 New Zealand
Search for more papers by this authorRichard J. Barker
Department of Mathematics and Statistics, University of Otago, P.O. Box 56, Dunedin , 9016 New Zealand
Search for more papers by this authorSummary
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.
Supporting Information
| Filename | Description |
|---|---|
| anzs12263-sup-0006-results4-19.RDataunknown, 1.5 KB |
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