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Annealed importance sampling reversible jump MCMC algorithms.

Karagiannis, G. and Andrieu, C. (2013) 'Annealed importance sampling reversible jump MCMC algorithms.', Journal of computational and graphical statistics., 22 (3). pp. 623-648.


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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphical Statistics on 20/09/2013, available online:
Date accepted:No date available
Date deposited:22 August 2017
Date of first online publication:20 September 2013
Date first made open access:No date available

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