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Annealed Importance Sampling Reversible Jump MCMC Algorithms

Karagiannis, G.; Andrieu, C.

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Authors

C. Andrieu



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

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