Annealed Importance Sampling Reversible Jump MCMC Algorithms

Karagiannis, G.; Andrieu, C.

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.

Citation

Karagiannis, G., & Andrieu, C. (2013). Annealed Importance Sampling Reversible Jump MCMC Algorithms. Journal of Computational and Graphical Statistics, 22(3), 623-648. https://doi.org/10.1080/10618600.2013.805651

• Journal Article Type: Article
• Online Publication Date: Sep 20, 2013
• Publication Date: Sep 20, 2013
• Deposit Date: Nov 10, 2016
• Publicly Available Date: Aug 22, 2017
• Journal: Journal of Computational and Graphical Statistics
• Print ISSN: 1061-8600
• Electronic ISSN: 1537-2715
• Publisher: American Statistical Association
• Peer Reviewed: Peer Reviewed
• Volume: 22
• Issue: 3
• Pages: 623-648
• DOI: https://doi.org/10.1080/10618600.2013.805651
• Public URL: https://durham-repository.worktribe.com/output/1393927