Statistical Science

Bayesian Experimental Design: A Review

Kathryn Chaloner and Isabella Verdinelli

Full-text: Open access

Abstract

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.

Article information

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

Dates
First available in Project Euclid: 19 April 2007

Permanent link to this document
http://projecteuclid.org/euclid.ss/1177009939

JSTOR
links.jstor.org

Digital Object Identifier
doi:10.1214/ss/1177009939

Mathematical Reviews number (MathSciNet)
MR1390519

Zentralblatt MATH identifier
0955.62617

Citation

Chaloner, Kathryn; Verdinelli, Isabella. Bayesian Experimental Design: A Review. Statistical Science 10 (1995), no. 3, 273--304. doi:10.1214/ss/1177009939. http://projecteuclid.org/euclid.ss/1177009939.


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