Bayesian experimental design for models with intractable likelihoods
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 concentration of the ABC posterior distribution, which we form efficiently via ABC rejection based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology on Markov process models for epidemics and macroparasite population evolution.
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|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
|Deposited On:||02 Oct 2012 13:57|
|Last Modified:||22 May 2013 14:05|
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