期刊
ANNALS OF STATISTICS
卷 37, 期 2, 页码 905-938出版社
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/07-AOS587
关键词
Amount of information; Bayesian asymptotics; consensus priors; Fisher information; Jeffreys priors; noninformative priors; objective priors; reference priors
资金
- NSF [DMS-01-03265, SES-0351523, SES-0720229]
- [MTM2006-07801]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0757549] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0757367, 0757527] Funding Source: National Science Foundation
Reference analysis produces objective Bayesian inference, in the sense that inferential statements depend only on the assumed model and the available data, and the prior distribution used to make an inference is least informative in a certain information-theoretic sense. Reference priors have been rigorously defined in specific contexts and heuristically defined in general, but a rigorous general definition has been lacking. We produce a rigorous general definition here and then show how an explicit expression for the reference prior can be obtained under very weak regularity conditions. The explicit expression can be used to derive new reference priors both analytically and numerically.
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