rjmcmc

R package rjmcmc: Reversible-Jump MCMC Using Post-Processing. Performs reversible-jump Markov chain Monte Carlo (Green, 1995) <doi:10.2307/2337340>, specifically the restriction introduced by Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising a ’universal parameter’ space, RJMCMC is treated as a Gibbs sampling problem. Previously-calculated posterior distributions are used to quickly estimate posterior model probabilities. Jacobian matrices are found using automatic differentiation.

Keywords for this software

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References in zbMATH (referenced in 4 articles , 1 standard article )

Showing results 1 to 4 of 4.
Sorted by year (citations)

  1. Gelling, Nicholas; Schofield, Matthew R.; Barker, Richard J.: \textsfRpackage \textsfrjmcmc: reversible jump MCMC using post-processing (2019)
  2. Heck, Daniel W.; Overstall, Antony M.; Gronau, Quentin F.; Wagenmakers, Eric-Jan: Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models (2019)
  3. Li, Le; Guedj, Benjamin; Loustau, Sébastien: A quasi-Bayesian perspective to online clustering (2018)
  4. Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers: bridgesampling: An R Package for Estimating Normalizing Constants (2017) arXiv