Information optimality and Bayesian modelling

Bertrand S Clarke

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

The general approach of treating a statistical problem as one of information processing led to the Bayesian method of moments, reference priors, minimal information likelihoods, and stochastic complexity. These techniques rest on quantities that have physical interpretations from information theory. Current work includes: the role of prediction, the emergence of data dependent priors, the role of information measures in model selection, and the use of conditional mutual information to incorporate partial information.

Original languageEnglish (US)
Pages (from-to)405-429
Number of pages25
JournalJournal of Econometrics
Volume138
Issue number2
DOIs
StatePublished - Jun 1 2007

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Stochastic Complexity
Reference Prior
Measures of Information
Bayesian Modeling
Partial Information
Dependent Data
Method of Moments
Bayesian Methods
Information Theory
Mutual Information
Information Processing
Model Selection
Likelihood
Optimality
Prediction
Interpretation
Bayesian modeling

Keywords

  • Bayesian method of moments
  • Data dependent priors
  • Entropy
  • Reference priors
  • Stochastic complexity

ASJC Scopus subject areas

  • Economics and Econometrics
  • Applied Mathematics

Cite this

Information optimality and Bayesian modelling. / Clarke, Bertrand S.

In: Journal of Econometrics, Vol. 138, No. 2, 01.06.2007, p. 405-429.

Research output: Contribution to journalArticle

Clarke, Bertrand S. / Information optimality and Bayesian modelling. In: Journal of Econometrics. 2007 ; Vol. 138, No. 2. pp. 405-429.
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