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Efficient construction of Bayes optimal designs for stochastic process models

Lookup NU author(s): Dr Colin GillespieORCiD, Professor Richard Boys

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by Springer New York LLC, 2019.

For re-use rights please refer to the publisher's terms and conditions.

Abstract

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Stochastic process models are now commonly used to analyse complex biological, ecological and industrial systems. Increasingly there is a need to deliver accurate estimates of model parameters and assess model fit by optimizing the timing of measurement of these processes. Standard methods to construct Bayes optimal designs, such the well known Müller algorithm, are computationally intensive even for relatively simple models. A key issue is that, in determining the merit of a design, the utility function typically requires summaries of many parameter posterior distributions, each determined via a computer-intensive scheme such as MCMC. This paper describes a fast and computationally efficient scheme to determine optimal designs for stochastic process models. The algorithm compares favourably with other methods for determining optimal designs and can require up to an order of magnitude fewer utility function evaluations for the same accuracy in the optimal design solution. It benefits from being embarrassingly parallel and is ideal for running on multi-core computers. The method is illustrated by determining different sized optimal designs for three problems of increasing complexity.

Publication metadata

Author(s): Gillespie CS, Boys RJ

Publication type: Article

Publication status: Published

Journal: Statistics and Computing

Year: 2019

Volume: 29

Issue: 4

Pages: 697-706

Print publication date: 01/07/2019

Online publication date: 19/09/2018

Acceptance date: 11/09/2018

Date deposited: 06/11/2018

ISSN (print): 0960-3174

ISSN (electronic): 1573-1375

Publisher: Springer New York LLC

URL: https://doi.org/10.1007/s11222-018-9833-3

DOI: 10.1007/s11222-018-9833-3

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