Sequential Monte Carlo samplers

• 成果类型: Article
• 署名作者: Del Moral, Pierre; Doucet, Arnaud; Jasra, Ajay
• 署名单位: University of British Columbia; University of British Columbia; Universite Cote d'Azur; University of Cambridge
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2006.00553.x
• 发表日期: 2006
• 页码: 411-436
• 关键词: particle filter; inference; models

摘要

We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time by using sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make parallel Markov chain Monte Carlo algorithms interact to perform global optimization and sequential Bayesian estimation and to compute ratios of normalizing constants. We illustrate these algorithms for various integration tasks arising in the context of Bayesian inference.