Authors: Dette, Holger
Guchenko, Roman
Melas, Viatcheslav B.
Title: Efficient computation of Bayesian optimal discriminating designs
Language (ISO): en
Abstract: An efficient algorithm for the determination of Bayesian optimal discriminating designs for competing regression models is developed, where the main focus is on models with general distributional assumptions beyond the \classical" case of normally distributed homoscedastic errors. For this purpose we consider a Bayesian version of the Kullback- Leibler (KL) optimality criterion introduced by Lopez-Fidalgo et al. (2007). Discretizing the prior distribution leads to local KL-optimal discriminating design problems for a large number of competing models. All currently available methods either require a large computation time or fail to calculate the optimal discriminating design, because they can only deal efficiently with a few model comparisons. In this paper we develop a new algorithm for the determination of Bayesian optimal discriminating designs with respect to the Kullback-Leibler criterion. It is demonstrated that the new algorithm is able to calculate the optimal discriminating designs with reasonable accuracy and computational time in situations where all currently available procedures are either slow or fail.
Subject Headings: design of experiment
Kullback-Leibler distance
model uncertainty
gradient methods
model discrimination
Bayesian optimal design
URI: http://hdl.handle.net/2003/34157
http://dx.doi.org/10.17877/DE290R-7518
Issue Date: 2015
Appears in Collections:Sonderforschungsbereich (SFB) 823

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