ITERATED FILTERING

• 成果类型: Article
• 署名作者: Ionides, Edward L.; Bhadra, Anindya; Atchade, Yves; King, Aaron
• 署名单位: University of Michigan System; University of Michigan; National Institutes of Health (NIH) - USA; NIH Fogarty International Center (FIC); University of Michigan System; University of Michigan
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/11-AOS886
• 发表日期: 2011
• 页码: 1776-1802
• 关键词: time-series analysis; MONTE-CARLO METHODS; inference; measles; models; algorithms; DYNAMICS; populations

摘要

Inference for partially observed Markov process models has been a long-standing methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of filtering problems. We present new theoretical results pertaining to the convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This theory complements the growing body of empirical evidence that iterated filtering algorithms provide an effective inference strategy for scientific models of nonlinear dynamic systems. The first step in our theory involves studying a new recursive approach for maximizing the likelihood function of a latent variable model, when this likelihood is evaluated via importance sampling. This leads to the consideration of an iterated importance sampling algorithm which serves as a simple special case of iterated filtering, and may have applicability in its own right.