Adaptive approximate Bayesian computation

• 成果类型: Article
• 署名作者: Beaumont, Mark A.; Cornuet, Jean-Marie; Marin, Jean-Michel; Robert, Christian P.
• 署名单位: University of Reading; Imperial College London; Universite de Montpellier; Universite PSL; Universite Paris-Dauphine
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asp052
• 发表日期: 2009
• 页码: 983990
• 关键词: sequential monte-carlo; population; likelihoods

摘要

Sequential techniques can enhance the efficiency of the approximate Bayesian computation algorithm, as in Sisson et al.'s (2007) partial rejection control version. While this method is based upon the theoretical works of Del Moral et al. (2006), the application to approximate Bayesian computation results in a bias in the approximation to the posterior. An alternative version based on genuine importance sampling arguments bypasses this difficulty, in connection with the population Monte Carlo method of Cappe et al. (2004), and it includes an automatic scaling of the forward kernel. When applied to a population genetics example, it compares favourably with two other versions of the approximate algorithm.