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

Elizabeth G. Ryan, Christopher C. Drovandi, M. Helen Thompson, Anthony N. Pettitt

Research output: Contribution to journalArticleResearchpeer-review

34 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)45-60
Number of pages16
JournalComputational Statistics and Data Analysis
Volume70
DOIs
Publication statusPublished - Feb 2014
Externally publishedYes

Keywords

  • Bayesian optimal design
  • Markov chain Monte Carlo
  • Robust design
  • Sampling strategies
  • Stochastic optimisation

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