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Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

Drovandi, Christopher C., McGree, James, & Pettitt, Anthony N. (2013) Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data. Computational Statistics and Data Analysis, 57(1).

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Abstract

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to generalised non-linear models for discrete data. The approach is computationally convenient in that the information of newly observed data can be incorporated through a simple re-weighting step. We also consider a flexible parametric model for the stimulus-response relationship together with a newly developed hybrid design utility that can produce more robust estimates of the target stimulus in the presence of substantial model and parameter uncertainty. The algorithm is applied to hypothetical clinical trial or bioassay scenarios. In the discussion, potential generalisations of the algorithm are suggested to possibly extend its applicability to a wide variety of scenarios

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6 citations in Scopus
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5 citations in Web of Science®

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ID Code: 48001
Item Type: Journal Article
Keywords: Particle filter, Sequential design, Sequential Monte Carlo, Target stimulus
DOI: 10.1016/j.csda.2012.05.014
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2012 Elsevier
Copyright Statement: This the author’s version of a work that was accepted for publication in Computational Statistics & 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 & Data Analysis, Volume 57, Issue 1, January 2013, Pages 320–335. DOI: 10.1016/j.csda.2012.05.014
Deposited On: 11 Jan 2012 08:42
Last Modified: 18 Jan 2013 10:48

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