Annealed importance sampling reversible jump MCMC algorithms

Georgios Karagiannis, Christophe Andrieu

Research output: Contribution to journalArticle (Academic Journal)

27 Citations (Scopus)

Abstract

We develop a methodology to efficiently implement the reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithms of Green, applicable for example to model selection inference in a Bayesian framework, which builds on the "dragging fast variables" ideas of Neal. We call such algorithms annealed importance sampling reversible jump (aisRJ). The proposed procedures can be thought of as being exact approximations of idealized RJ algorithms which in a model selection problem would sample the model labels only, but cannot be implemented. Central to the methodology is the idea of bridging different models with fictitious intermediate models, whose role is to introduce smooth intermodel transitions and, as we shall see, improve performance. Efficiency of the resulting algorithms is demonstrated on two standard model selection problems and we show that despite the additional computational effort incurred, the approach can be highly competitive computationally. Supplementary materials for the article are available online.

Original languageEnglish
Pages (from-to)623-648
Number of pages26
JournalJournal of Computational and Graphical Statistics
Volume22
Issue number3
DOIs
Publication statusPublished - 2013

Keywords

  • Bayesian model selection/determination
  • Gaussian mixture models
  • Poisson change point problem
  • Pseudo-marginal MCMC

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