Statistical Science

Bayesian Experimental Design: A Review

Kathryn Chaloner and Isabella Verdinelli

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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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Statist. Sci., Volume 10, Number 3 (1995), 273-304.

First available in Project Euclid: 19 April 2007

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Decision theory hierarchical linear models logistic regression nonlinear design nonlinear models optimal design optimality criteria utility functions


Chaloner, Kathryn; Verdinelli, Isabella. Bayesian Experimental Design: A Review. Statist. Sci. 10 (1995), no. 3, 273--304. doi:10.1214/ss/1177009939.

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