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.
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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|Item Type:||Conference Paper|
|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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