A minimally informative likelihood for decision analysis: Illustration and robustness

Ao Yuan, Bertrand S. Clarke

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The authors discuss a class of likelihood functions involving weak assumptions on data generating mechanisms. These likelihoods may be appropriate when it is difficult to propose models for the data. The properties of these likelihoods are given and it is shown how they can be computed numerically by use of the Blahut-Arimoto algorithm. The authors then show how these likelihoods can give useful inferences using a data set for which no plausible physical model is apparent. The plausibility of the inferences is enhanced by the extensive robustness analysis these likelihoods permit.

Original languageEnglish (US)
Pages (from-to)649-665
Number of pages17
JournalCanadian Journal of Statistics
Issue number3
StatePublished - Sep 1999


  • Blahut-Arimoto algorithm
  • Information
  • Rate distortion function
  • Robustness

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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