On obtaining invariant prior distributions

Edward I. George, Robert McCulloch

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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
Issue number2
StatePublished - Nov 1993
Externally publishedYes


  • 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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