Towards Bayesian experimental design for nonlinear models that require a large number of sampling times

Ryan, Elizabeth, Drovandi, Christopher C., Thompson, Helen, & Pettitt, Anthony N. (2014) Towards Bayesian experimental design for nonlinear models that require a large number of sampling times. Computational Statistics and Data Analysis, 70, pp. 45-60.

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The use of Bayesian methodologies for solving optimal experimental design problems has increased. Many of these methods have been found to be computationally intensive for design problems that require a large number of design points. A simulation-based approach that can be used to solve optimal design problems in which one is interested in finding a large number of (near) optimal design points for a small number of design variables is presented. The approach involves the use of lower dimensional parameterisations that consist of a few design variables, which generate multiple design points. Using this approach, one simply has to search over a few design variables, rather than searching over a large number of optimal design points, thus providing substantial computational savings. The methodologies are demonstrated on four applications, including the selection of sampling times for pharmacokinetic and heat transfer studies, and involve nonlinear models. Several Bayesian design criteria are also compared and contrasted, as well as several different lower dimensional parameterisation schemes for generating the many design points.

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4 citations in Scopus
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5 citations in Web of Science®

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ID Code: 56522
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Bayesian optimal design, High dimensional design, Robust design, Markov chain Monte Carlo, Stochastic optimisation
DOI: 10.1016/j.csda.2013.08.017
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistics not elsewhere classified (010499)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis, Vol 70 (2014) DOI: 10.1016/j.csda.2013.08.017
Deposited On: 21 Jan 2013 00:00
Last Modified: 01 Feb 2016 18:43

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