1971/02/01 by James M. Dickey, James Dickey · 47 citations
Mathematics · Decision Sciences · #Statistical Distribution Estimation and Applications #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring
paper · doi:10.1214/aoms/1177693507
Raiffa and Schlaifer's theory of conjugate prior distributions is here applied to Jeffrey's theory of tests for a sharp hypothesis, for simple normal sampling, for model I analysis of variance, and for univariate and multivariate Behrens-Fisher probelms. Leonard J. Savage's Bayesianization of Jeffrey's theory is given with new generalizations. A new conjugate prior family for normal sampling which allows prior independence of unknown mena and variance is given.