The Annals of Statistics

Expected Information as Expected Utility

Jose M. Bernardo

Full-text: Open access

Abstract

The normative procedure for the design of an experiment is to select a utility function, assess the probabilities, and to choose that design of maximum expected utility. One difficulty with this view is that a scientist typically does not have, nor can be normally expected to have, a clear idea of the utility of his results. An alternative is to design an experiment to maximize the expected information to be gained from it. In this paper we show that the latter view is a special case of the former with an appropriate choice of the decision space and a reasonable constraint on the utility function. In particular, the Shannon concept of information is seen to play a more important role in experimental design than was hitherto thought possible.

Article information

Source
Ann. Statist. Volume 7, Number 3 (1979), 686-690.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176344689

Digital Object Identifier
doi:10.1214/aos/1176344689

Mathematical Reviews number (MathSciNet)
MR527503

Zentralblatt MATH identifier
0407.62002

JSTOR
links.jstor.org

Subjects
Primary: 62A15
Secondary: 62B15: Theory of statistical experiments 62B10: Information-theoretic topics [See also 94A17]

Keywords
Bayesian statistics decision theory design of experiments information scientific inference utility

Citation

Bernardo, Jose M. Expected Information as Expected Utility. Ann. Statist. 7 (1979), no. 3, 686--690. doi:10.1214/aos/1176344689. https://projecteuclid.org/euclid.aos/1176344689


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