Efficient Bayesian design for discriminating between models with intractable likelihoods in epidemiology

Dehideniya, Mahasen Bandara, Drovandi, Christopher C., & McGree, James (2016) Efficient Bayesian design for discriminating between models with intractable likelihoods in epidemiology. [Working Paper] (Unpublished)


In this work, we propose a methodology for deriving Bayesian experimental designs for discriminating between rival epidemiological models with computationally intractable likelihoods. Our approach uses methods from approximate Bayesian computation to facilitate Bayesian inference in this setting, and we show how this algorithm can be implemented efficiently to reduce the required computational effort in evaluating the utility of a given design. We consider three utility functions for model discrimination and explore the performance of these utilities for discriminating between three epidemiological models; the death model, the Susceptible-Infected model, and the Susceptible-Exposed-Infective model. The optimisation challenge of efficiently locating an optimal design is addressed by a novel adaptation of the coordinate exchange algorithm which exploits parallel computational architectures.

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ID Code: 97824
Item Type: Working Paper
Refereed: No
Keywords: Approximate Bayesian computation, Ds-Optimality, Model discrimination, Mutual information, Parameter estimation, Coordinate exchange algorithm, Simulated annealing, Zero-One utility
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2016 The Author(s)
Deposited On: 05 Aug 2016 00:11
Last Modified: 21 Jul 2017 19:51

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