Approximate Bayesian computation using auxiliary model based estimates

Pettitt, Anthony N., Drovandi, Christopher C., & Faddy, Malcolm (2010) Approximate Bayesian computation using auxiliary model based estimates. In Bowman, Adrian (Ed.) Proceedings of the 25th International Workshop on Statistical Modelling, University of Glasgow, Glasgow, pp. 433-438.

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We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms using indirect infer- ence. We embed this approach within a sequential Monte Carlo algorithm that is completely adaptive. This methodological development was motivated by an application involving data on macroparasite population evolution modelled with a trivariate Markov process. The main objective of the analysis is to compare inferences on the Markov process when considering two di®erent indirect mod- els. The two indirect models are based on a Beta-Binomial model and a three component mixture of Binomials, with the former providing a better ¯t to the observed data.

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ID Code: 69026
Item Type: Conference Paper
Refereed: Yes
Keywords: Approximate Bayesian computation, Beta-Binomial model, Binomial mixture model, Indirect inference, Markov process, Sequential Monte Carlo
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2010 [please consult the author]
Deposited On: 23 Mar 2014 23:24
Last Modified: 19 Jun 2014 23:33

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