文章基本信息

标题：Adaptive Approximate Bayesian Computation Tolerance Selection
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作者：Umberto Simola ; Jessi Cisewski-Kehe ; Michael U. Gutmann 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2021
卷号：16
期号：2
页码：397-423
DOI：10.1214/20-BA1211
语种：English
出版社：International Society for Bayesian Analysis
摘要：Approximate Bayesian Computation (ABC) methods are increasingly used for inference in situations in which the likelihood function is either computationally costly or intractable to evaluate. Extensions of the basic ABC rejection algorithm have improved the computational efficiency of the procedure and broadened its applicability. The ABC – Population Monte Carlo (ABC-PMC) approach has become a popular choice for approximate sampling from the posterior. ABC-PMC is a sequential sampler with an iteratively decreasing value of the tolerance, which specifies how close the simulated data need to be to the real data for acceptance. We propose a method for adaptively selecting a sequence of tolerances that improves the computational efficiency of the algorithm over other common techniques. In addition we define a stopping rule as a by-product of the adaptation procedure, which assists in automating termination of sampling. The proposed automatic ABC-PMC algorithm can be easily implemented and we present several examples demonstrating its benefits in terms of computational efficiency.
关键词：complex stochastic modeling; likelihood-free methods; sequential Monte Carlo