A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL‐mapping

Rasmus Waagepetersen and Daniel Sorensen

International Statistical Review, 2001, vol. 69, issue 1, 49-61

Abstract: A tutorial derivation of the reversible jump Markov chain Monte Carlo (MCMC) algorithm is given. Various examples illustrate how reversible jump MCMC is a general framework for Metropolis‐Hastings algorithms where the proposal and the target distribution may have densities on spaces of varying dimension. It is finally discussed how reversible jump MCMC can be applied in genetics to compute the posterior distribution of the number, locations, effects, and genotypes of putative quantitative trait loci.

Date: 2001
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