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Approximate Bayesian computation via the energy statistic

By Hien Nguyen, Julyan Arbel, Hongliang Lu and Florence Forbes

Abstract

Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely unknown, making it intractable. ABC defines a quasi-posterior by comparing observed data with simulated data, traditionally based on some summary statistics, the elicitation of which is regarded as a key difficulty. In recent years, a number of data discrepancy measures bypassing the construction of summary statistics have been proposed, including the Kullback-Leibler divergence, the Wasserstein distance and maximum mean discrepancies. Here we propose a novel importance-sampling (IS) ABC algorithm relying on the so-called two-sample energy statistic. We establish a new asymptotic result for the case where both the observed sample size and the simulated data sample size increase to infinity, which highlights to what extent the data discrepancy measure impacts the asymptotic pseudo-posterior. The result holds in the broad setting of IS-ABC methodologies, thus generalizing previous results that have been established only for rejection ABC algorithms. Furthermore, we propose a consistent V-statistic estimator of the energy statistic, under which we show that the large sample result holds. Our proposed energy statistic based ABC algorithm is demonstrated on a variety of models, including a Gaussian mixture, a moving-average model of order two, a bivariate beta and a multivariate g-and-k distribution. We find that our proposed method compares well with alternative discrepancy measures

Topics: Wasserstein distance, maximum mean discrepancy, approximate Bayesian computation, energy statistic, Kullback-Leibler divergence, importance sampling, [STAT.CO]Statistics [stat]/Computation [stat.CO], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.ME]Statistics [stat]/Methodology [stat.ME]
Publisher: HAL CCSD
Year: 2019
OAI identifier: oai:HAL:hal-02399934v1
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