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.

View at publisher


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:

25 citations in Scopus
15 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

201 since deposited on 10 Mar 2011
15 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 40624
Item Type: Journal Article
Refereed: Yes
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)
Copyright Owner: Copyright 2011 Royal Statistical Society
Deposited On: 10 Mar 2011 01:54
Last Modified: 03 Jan 2012 16:34

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page