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Approximate Bayesian computation using indirect inference

Drovandi, Christopher C., Pettitt, Anthony N., & Faddy, Malcolm J. (2011) Approximate Bayesian computation using indirect inference. Journal of the Royal Statistical Society, Series C (Applied Statistics), 60(3), pp. 317-337.

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Abstract

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms by using indirect inference. ABC methods are useful for posterior inference in the presence of an intractable likelihood function. In the indirect inference approach to ABC the parameters of an auxiliary model fitted to the data become the summary statistics. Although applicable to any ABC technique, we embed this approach within a sequential Monte Carlo algorithm that is completely adaptive and requires very little tuning. This methodological development was motivated by an application involving data on macroparasite population evolution modelled by a trivariate stochastic process for which there is no tractable likelihood function. The auxiliary model here is based on a beta–binomial distribution. The main objective of the analysis is to determine which parameters of the stochastic model are estimable from the observed data on mature parasite worms.

Impact and interest:

11 citations in Scopus
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7 citations in Web of Science®

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126 since deposited on 10 Mar 2011
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ID Code: 40624
Item Type: Journal Article
Keywords: Approximate Bayesian computation, Beta–binomial model, Indirect inference, Macroparasite, Markov process, Sequential Monte Carlo methods
DOI: 10.1111/j.1467-9876.2010.00747.x
ISSN: 0035-9254
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2011 Royal Statistical Society
Deposited On: 10 Mar 2011 11:54
Last Modified: 04 Jan 2012 02:34

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