The DOI matches its Crossref record; 146 of the 146 references it cites were found in a registry or an index.
The Selection of Prior Distributions by Formal Rules
Abstract
Subjectivism has become the dominant philosophical foundation for Bayesian inference. Yet in practice, most Bayesian analyses are performed with so-called “noninformative” priors, that is, priors constructed by some formal rule. We review the plethora of techniques for constructing such priors and discuss some of the practical and philosophical issues that arise when they are used. We give special emphasis to Jeffreys's rules and discuss the evolution of his viewpoint about the interpretation of priors, away from unique representation of ignorance toward the notion that they should be chosen by convention. We conclude that the problems raised by the research on priors chosen by formal rules are serious and may not be dismissed lightly: When sample sizes are small (relative to the number of parameters being estimated), it is dangerous to put faith in any “default” solution; but when asymptotics take over, Jeffreys's rules and their variants remain reasonable choices. We also provide an annotated bibliography.
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
An Analysis of Transformations
Box, G. E. P., & Cox, D. R. (1964) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1964.tb00553.x · Cited by 15,052
Information theory and statistical mechanics
Jaynes, E. T. (1957) · Physical Review · doi:10.1103/physrev.106.620 · Cited by 12,931
A Mathematical Theory of Evidence
Shafer, G. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv10vm1qb · Cited by 11,711
A Mathematical Theory of Evidence
Shafer, G. (1976) · Princeton University Press eBooks · doi:10.1515/9780691214696 · Cited by 9,083
Jeffreys, H., Moran, P. A. P., & Chatfield, C. (1922) · Nature · doi:10.1093/oso/9780198503682.001.0001 · Cited by 7,878
Statistical Decision Theory and Bayesian Analysis
Berger, J. O. (1985) · Springer Series in Statistics · doi:10.1007/978-1-4757-4286-2 · Cited by 7,722
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
(2001) · Empirical Direction in Design and Analysis · doi:10.4324/9781410600004-24 · Cited by 6,808
Cited by
Gelman, A. (2006) · Bayesian Analysis · doi:10.1214/06-ba117a · Cited by 4,050
A practical solution to the pervasive problems ofp values
Wagenmakers, E.-J. (2007) · Psychonomic Bulletin & Review · doi:10.3758/bf03194105 · Cited by 2,490
Using Bayes to get the most out of non-significant results
Dienes, Z. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00781 · Cited by 1,998
(2013) · Cited by 1,351
Anderson, G. B., & Bell, M. L. (2011) · Environmental Health Perspectives · doi:10.1289/ehp.1002313 · Cited by 1,191
Metadata
- Authors
- Kass, R. E., & Wasserman, L.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1996-09
- Volume
- 91
- Issue
- 435
- Pages
- 1343-1370
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2139606141
- Cited by
- 1,308
- References
- 146
- FWCI
- 18.21
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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