Efficient computation of Bayesian optimal discriminating designs
Loading...
Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
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.
Description
Table of contents
Keywords
design of experiment, Kullback-Leibler distance, model uncertainty, gradient methods, model discrimination, Bayesian optimal design