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Bayesian experimental design for models with intractable likelihoods

Drovandi, Christopher C. & Pettitt, Anthony N. (2013) Bayesian experimental design for models with intractable likelihoods. Biometrics, 69(4), pp. 937-948.

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    Abstract

    In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables.

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    ID Code: 53924
    Item Type: Journal Article
    Keywords: Approximate Bayesian computation, Bayesian experimental design, Markov chain Monte Carlo, Robust experimental design
    DOI: 10.1111/biom.12081
    ISSN: 1541-0420
    Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
    Divisions: Current > Schools > School of Mathematical Sciences
    Current > QUT Faculties and Divisions > Science & Engineering Faculty
    Copyright Owner: Copyright 2013 The International Biometric Society
    Copyright Statement: The definitive version is available at www3.interscience.wiley.com
    Deposited On: 02 Oct 2012 13:57
    Last Modified: 21 Feb 2014 00:25

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