文章基本信息

标题：Sequential Monte Carlo Samplers with Independent Markov Chain Monte Carlo Proposals
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作者：L. F. South ; A. N. Pettitt ; C. C. Drovandi 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2019
卷号：14
期号：3
页码：753-776
DOI：10.1214/18-BA1129
语种：English
出版社：International Society for Bayesian Analysis
摘要：Sequential Monte Carlo (SMC) methods for sampling from the posterior of static Bayesian models are flexible, parallelisable and capable of handling complex targets. However, it is common practice to adopt a Markov chain Monte Carlo (MCMC) kernel with a multivariate normal random walk (RW) proposal in the move step, which can be both inefficient and detrimental for exploring challenging posterior distributions. We develop new SMC methods with independent proposals which allow recycling of all candidates generated in the SMC process and are embarrassingly parallelisable. A novel evidence estimator that is easily computed from the output of our independent SMC is proposed. Our independent proposals are constructed via flexible copula-type models calibrated with the population of SMC particles. We demonstrate through several examples that more precise estimates of posterior expectations and the marginal likelihood can be obtained using fewer likelihood evaluations than the more standard RW approach.
关键词：copula; evidence; importance sampling; independent proposal;Markov chain Monte Carlo; marginal likelihood.