Efficient block sampling strategies for sequential Monte Carlo methods

Journal article

Sequential Monte Carlo (SMC) methods are a powerful set of simulation-based techniques for sampling sequentially from a sequence of complex probability distributions. These methods rely on a combination of importance sampling and resampling techniques. In a Markov chain Monte Carlo (MCMC) framework, block sampling strategies often perform much better than algorithms based on one-at-a-time sampling strategies if "good" proposal distributions to update blocks of variables can be designed. In an SMC framework, standard algorithms sequentially sample the variables one at a time whereas, like MCMC, the efficiency of algorithms could be improved significantly by using block sampling strategies. Unfortunately, a direct implementation of such strategies is impossible as it requires the knowledge of integrals which do not admit closed-form expressions. This article introduces a new methodology which by-passes this problem and is a natural extension of standard SMC methods. Applications to several sequential Bayesian inference problems demonstrate these methods. © 2006 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Authors: Doucet, A; Briers, M; Stephane, S

• Publication status: Published
• Journal: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
• Volume: 15
• Issue: 3
• Pages: 693-711
• Publication date: 2006-09-01
• DOI: 10.1198/106186006X142744
• EISSN: 1537-2715
• ISSN: 1061-8600
• Language: English
• Keywords: importance sampling; optimal filtering; state-space models; Markov chain Monte Carlo; block sequential Monte Carlo; particle filtering