Adaptive Approximate Bayesian Computation Tolerance Selection

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Simola , U , Cisewski-Kehe , J , Gutmann , M U & Corander , J 2021 , ' Adaptive Approximate Bayesian Computation Tolerance Selection ' , Bayesian analysis , vol. 16 , no. 2 , pp. 397-423 .

Title: Adaptive Approximate Bayesian Computation Tolerance Selection
Author: Simola, Umberto; Cisewski-Kehe, Jessi; Gutmann, Michael U.; Corander, Jukka
Contributor: University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, University of Oslo
Date: 2021-06
Language: eng
Number of pages: 27
Belongs to series: Bayesian analysis
ISSN: 1931-6690
Abstract: 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.
Subject: 111 Mathematics
complex stochastic modeling
likelihood-free methods
sequential Monte Carlo

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