Bayesian experimental design for models with intractable likelihoods
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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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|Item Type:||Journal Article|
|Additional Information:||Early View (Online Version of Record published before inclusion in an issue)|
|Keywords:||Approximate Bayesian computation, Bayesian experimental design, Markov chain Monte Carlo, Robust experimental design|
|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:||23 Nov 2013 22:07|
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