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August, 1995 Bayesian Experimental Design: A Review
Kathryn Chaloner, Isabella Verdinelli
Statist. Sci. 10(3): 273-304 (August, 1995). DOI: 10.1214/ss/1177009939


This paper reviews the literature on Bayesian experimental design. A unified view of this topic is presented, based on a decision-theoretic approach. This framework casts criteria from the Bayesian literature of design as part of a single coherent approach. The decision-theoretic structure incorporates both linear and nonlinear design problems and it suggests possible new directions to the experimental design problem, motivated by the use of new utility functions. We show that, in some special cases of linear design problems, Bayesian solutions change in a sensible way when the prior distribution and the utility function are modified to allow for the specific structure of the experiment. The decision-theoretic approach also gives a mathematical justification for selecting the appropriate optimality criterion.


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Kathryn Chaloner. Isabella Verdinelli. "Bayesian Experimental Design: A Review." Statist. Sci. 10 (3) 273 - 304, August, 1995.


Published: August, 1995
First available in Project Euclid: 19 April 2007

zbMATH: 0955.62617
MathSciNet: MR1390519
Digital Object Identifier: 10.1214/ss/1177009939

Keywords: decision theory , hierarchical linear models , logistic regression , nonlinear design , nonlinear models , optimal design , optimality criteria , utility functions

Rights: Copyright © 1995 Institute of Mathematical Statistics

Vol.10 • No. 3 • August, 1995
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