title: Bayesian experimental design for models with intractable likelihoods.
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.
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Drovandi
authors.forename: Anthony N
authors.initials: AN
authors.lastname: Pettitt
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012107:Research Design; D013269:Stochastic Processes
keywords: Approximate Bayesian computation; Bayesian experimental design; Markov chain Monte Carlo; Robust experimental design