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.
"The Weighted Likelihood Ratio, Linear Hypotheses on Normal Location Parameters." Ann. Math. Statist. 42 (1) 204 - 223, February, 1971. https://doi.org/10.1214/aoms/1177693507