An Invitation to Sequential Monte Carlo Samplers

• 成果类型: Article
• 署名作者: Dai, Chenguang; Heng, Jeremy; Jacob, Pierre E.; Whiteley, Nick
• 署名单位: ESSEC Business School; University of Bristol
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2022.2087659
• 发表日期: 2022
• 页码: 1587-1600
• 关键词: normalizing constants; variance-estimation; inference; algorithm; models; CONVERGENCE; simulation; bounds

摘要

Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits. Supplementary materials for this article are available online.