Adaptive importance sampling on discrete Markov chains

• 成果类型: Article
• 署名作者: Kollman, C; Baggerly, K; Cox, D; Picard, R
• 署名单位: National Marrow Donor Program; Rice University; United States Department of Energy (DOE); Los Alamos National Laboratory
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• 发表日期: 1999
• 页码: 391-412
• 关键词: monte-carlo

摘要

In modeling particle transport through a medium, the path of a particle behaves as a transient Markov chain. We are interested in characteristics of the particle's movement conditional on its starting state, which take the form of a score accumulated with each transition. Importance sampling is an essential variance reduction technique in this setting, and we provide an adaptive (iteratively updated) importance sampling algorithm that converges exponentially to the solution. Examples illustrating this phenomenon are provided.