Title: Efficient Block Sampling Strategies for Sequential Monte Carlo Methods
Abstract: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 combinati...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.Read More
Publication Year: 2006
Publication Date: 2006-09-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 143
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