Asymptotics of ABC

2017/06/23 by Paul Fearnhead, Fearnhead, Paul · 1 citation
Computer Science · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Polynomial and algebraic computation #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.1706.07712

openalex publication_date 2017/06/23 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28

Abstract

We present an informal review of recent work on the asymptotics of Approximate Bayesian Computation (ABC). In particular we focus on how does the ABC posterior, or point estimates obtained by ABC, behave in the limit as we have more data? The results we review show that ABC can perform well in terms of point estimation, but standard implementations will over-estimate the uncertainty about the parameters. If we use the regression correction of Beaumont et al. then ABC can also accurately quantify this uncertainty. The theoretical results also have practical implications for how to implement ABC.

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