Description Functions Methods References
Performs reversible-jump MCMC (Green, 1995), specifically the reformulation
introduced by Barker & Link (2013). Using a 'universal parameter' space,
RJMCMC is treated as Gibbs sampling making it simpler to implement. The
required Jacobian matrices are calculated automatically, utilising the
madness
package.
rjmcmcpost
defaultpost
adiff
getsampler
rjmethods
Green, P. J. (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 711-732.
Barker, R. J. and Link, W. A. (2013) Bayesian multimodel inference by RJMCMC: A Gibbs sampling approach. The American Statistician, 67(3), 150-156.
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