On obtaining invariant prior distributions

Edward I. George; Robert McCulloch

doi:10.1016/0378-3758(93)90086-L

On obtaining invariant prior distributions

Edward I. George, Robert McCulloch

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

This paper considers a generalization of the connection between Jeffreys prior and the Kullback-Leibler divergence as a procedure for generating a wide class of invariant priors of which Jeffreys prior is only one. By viewing Jeffreys' approach as a special case of a more general procedure, we can see that the choice of Jeffreys prior entails both parametrization invariance and sample space invariance. This general procedure also provides a link between distributional discrepancy measures and Haar measure.

Original language	English (US)
Pages (from-to)	169-179
Number of pages	11
Journal	Journal of Statistical Planning and Inference
Volume	37
Issue number	2
DOIs	https://doi.org/10.1016/0378-3758(93)90086-L
State	Published - Nov 1993
Externally published	Yes

Keywords

Divergence measures
Haar measure, information measures
Jeffreys prior
invariant priors

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1016/0378-3758(93)90086-L

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AB - This paper considers a generalization of the connection between Jeffreys prior and the Kullback-Leibler divergence as a procedure for generating a wide class of invariant priors of which Jeffreys prior is only one. By viewing Jeffreys' approach as a special case of a more general procedure, we can see that the choice of Jeffreys prior entails both parametrization invariance and sample space invariance. This general procedure also provides a link between distributional discrepancy measures and Haar measure.

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KW - Jeffreys prior

KW - invariant priors

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On obtaining invariant prior distributions

Abstract

Keywords

ASJC Scopus subject areas

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