On obtaining invariant prior distributions

Edward I. George, Robert McCulloch

Research output: Contribution to journalArticle

12 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)169-179
Number of pages11
JournalJournal of Statistical Planning and Inference
Volume37
Issue number2
DOIs
StatePublished - 1993
Externally publishedYes

Fingerprint

Jeffreys Prior
Invariant Distribution
Prior distribution
Invariance
Sample space
Kullback-Leibler Divergence
Haar Measure
Parametrization
Discrepancy
Invariant

Keywords

  • Divergence measures
  • Haar measure, information measures
  • invariant priors
  • Jeffreys prior

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Statistics and Probability

Cite this

On obtaining invariant prior distributions. / George, Edward I.; McCulloch, Robert.

In: Journal of Statistical Planning and Inference, Vol. 37, No. 2, 1993, p. 169-179.

Research output: Contribution to journalArticle

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