Open Access
September 2016 Optimal Bayesian Experimental Design for Models with Intractable Likelihoods Using Indirect Inference Applied to Biological Process Models
Caitríona M. Ryan, Christopher C. Drovandi, Anthony N. Pettitt
Bayesian Anal. 11(3): 857-883 (September 2016). DOI: 10.1214/15-BA977


This paper addresses the problem of determining optimal designs for biological process models with intractable likelihoods, with the goal of parameter inference. The Bayesian approach is to choose a design that maximises the mean of a utility, and the utility is a function of the posterior distribution. Therefore, its estimation requires likelihood evaluations. However, many problems in experimental design involve models with intractable likelihoods, that is, likelihoods that are neither analytic nor can be computed in a reasonable amount of time. We propose a novel solution using indirect inference (II), a well established method in the literature, and the Markov chain Monte Carlo (MCMC) algorithm of Müller et al. (2004). Indirect inference employs an auxiliary model with a tractable likelihood in conjunction with the generative model, the assumed true model of interest, which has an intractable likelihood. Our approach is to estimate a map between the parameters of the generative and auxiliary models, using simulations from the generative model. An II posterior distribution is formed to expedite utility estimation. We also present a modification to the utility that allows the Müller algorithm to sample from a substantially sharpened utility surface, with little computational effort. Unlike competing methods, the II approach can handle complex design problems for models with intractable likelihoods on a continuous design space, with possible extension to many observations. The methodology is demonstrated using two stochastic models; a simple tractable death process used to validate the approach, and a motivating stochastic model for the population evolution of macroparasites.


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Caitríona M. Ryan. Christopher C. Drovandi. Anthony N. Pettitt. "Optimal Bayesian Experimental Design for Models with Intractable Likelihoods Using Indirect Inference Applied to Biological Process Models." Bayesian Anal. 11 (3) 857 - 883, September 2016.


Published: September 2016
First available in Project Euclid: 9 October 2015

zbMATH: 1359.62321
MathSciNet: MR3543911
Digital Object Identifier: 10.1214/15-BA977

Keywords: Approximate Bayesian Computation , auxiliary model , Bayesian experimental design , Indirect inference , Markov chain Monte Carlo , Markov processes

Rights: Copyright © 2016 International Society for Bayesian Analysis

Vol.11 • No. 3 • September 2016
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