The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Expected Information as Expected Utility
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.
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Citations
Cites
Scoring Rules and the Evaluation of Probability Assessors
Winkler, R. L. (1969) · Journal of the American Statistical Association · doi:10.1080/01621459.1969.10501037 · Cited by 271
Cited by
Bayesian Measures of Model Complexity and Fit
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793
Understanding predictive information criteria for Bayesian models
Gelman, A., Hwang, J., & Vehtari, A. (2014) · Statistics and Computing · doi:10.1007/s11222-013-9416-2 · Cited by 2,104
“The logarithmic score is the unique (up to an affine transformation) local and proper scoring rule (Bernardo, 1979), and appears to be the most commonly used scoring rule in model selection.”
BackgroundProbabilistic Forecasts, Calibration and Sharpness
Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00587.x · Cited by 1,783
The Selection of Prior Distributions by Formal Rules
Kass, R. E., & Wasserman, L. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10477003 · Cited by 1,308
Metadata
- Authors
- Bernardo, J. M.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1979-05-01
- Volume
- 7
- Issue
- 3
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2060442802
- Cited by
- 568
- References
- 1
- FWCI
- 2.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bernardo, J. M. (1979). Expected Information as Expected Utility. The Annals of Statistics. https://doi.org/10.1214/aos/1176344689
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
