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Diagnostic tools for approximate Bayesian computation using the coverage property

Identifieur interne : 000148 ( Istex/Checkpoint ); précédent : 000147; suivant : 000149

Diagnostic tools for approximate Bayesian computation using the coverage property

Auteurs : D. Prangle [Royaume-Uni] ; M. G. B. Blum [France] ; G. Popovic [Australie] ; S. A. Sisson [Australie]

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RBID : ISTEX:C939A1CF7BD18ECF3250E937CC7214BB4FD1807F

Abstract

Approximate Bayesian computation (ABC) is an approach to sampling from an approximate posterior distribution in the presence of a computationally intractable likelihood function. A common implementation is based on simulating model, parameter and dataset triples from the prior, and then accepting as samples from the approximate posterior, those model and parameter pairs for which the corresponding dataset, or a summary of that dataset, is ‘close’ to the observed data. Closeness is typically determined though a distance measure and a kernel scale parameter. Appropriate choice of that parameter is important in producing a good quality approximation. This paper proposes diagnostic tools for the choice of the kernel scale parameter based on assessing the coverage property, which asserts that credible intervals have the correct coverage levels in appropriately designed simulation settings. We provide theoretical results on coverage for both model and parameter inference, and adapt these into diagnostics for the ABC context. We re‐analyse a study on human demographic history to determine whether the adopted posterior approximation was appropriate. Code implementing the proposed methodology is freely available in the R package abctools.

Url:
DOI: 10.1111/anzs.12087


Affiliations:


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ISTEX:C939A1CF7BD18ECF3250E937CC7214BB4FD1807F

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