The Annals of Statistics

Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician

Donald B. Rubin

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A common reaction among applied statisticians is that the Bayesian statistician's energies in an applied problem must be directed at the a priori elicitation of one model specification from which an optimal design and all inferences follow automatically by applying Bayes's theorem to calculate conditional distributions of unknowns given knowns. I feel, however, that the applied Bayesian statistician's tool-kit should be more extensive and include tools that may be usefully labeled frequency calculations. Three types of Bayesianly justifiable and relevant frequency calculations are presented using examples to convey their use for the applied statistician.

Article information

Ann. Statist. Volume 12, Number 4 (1984), 1151-1172.

First available in Project Euclid: 12 April 2007

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


Primary: 62A15
Secondary: 62F15: Bayesian inference 62L10: Sequential analysis 62P99: None of the above, but in this section

62-07 Calibration empirical Bayes inference model monitoring operating characteristics posterior predictive checks stopping rules


Rubin, Donald B. Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician. Ann. Statist. 12 (1984), no. 4, 1151--1172. doi:10.1214/aos/1176346785.

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