Stratified distance space improves the efficiency of sequential samplers for approximate Bayesian computation

作     者：Pesonen, Henri Corander, Jukka 

作者机构：Oslo Univ Hosp Oslo Ctr Biostat & Epidemiol POB 4950 N-0424 Oslo Norway Univ Oslo Dept Biostat Oslo Norway Wellcome Sanger Inst Parasites & Microbes Hinxton Cambridgeshire England Univ Helsinki Helsinki Inst Informat Technol HIIT Dept Math & Stat Helsinki Finland 

出 版 物：《COMPUTATIONAL STATISTICS & DATA ANALYSIS》 (Comput. Stat. Data Anal.)

年 卷 期：2025年第207卷

核心收录：

学科分类：08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Research Council of Norway [299941  332645] 

主　　题：Approximate Bayesian computation Importance sampling Likelihood-free inference Sequential Monte Carlo 

摘      要：Approximate Bayesian computation (ABC) methods are standard tools for inferring parameters of complex models when the likelihood function is analytically intractable. A popular approach to improving the poor acceptance rate of the basic rejection sampling ABC algorithm is to use sequential Monte Carlo (ABC SMC) to produce a sequence of proposal distributions adapting towards the posterior, instead of generating values from the prior distribution of the model parameters. Proposal distribution for the subsequent iteration is typically obtained from a weighted set of samples, often called particles, of the current iteration of this sequence. Current methods for constructing these proposal distributions treat all the particles equivalently, regardless of the corresponding value generated by the sampler, which may lead to inefficiency when propagating the information across iterations of the algorithm. To improve sampler efficiency, a modified approach called stratified distance ABC SMC is introduced. The algorithm stratifies particles based on their distance between the corresponding synthetic and observed data, and then constructs distinct proposal distributions for all the strata. Taking into account the distribution of distances across the particle space leads to substantially improved acceptance rate of the rejection sampling. It is shown that further efficiency could be gained by using a newly proposed stopping rule for the sequential process based on the stratified posterior samples and these advances are demonstrated by several examples.