SOME MAXIMAL INFORMATION AND GENERALIZED MAXIMAL ENTROPY PRIORS

This paper is concerned with there procedures to derive prior distributions with knowledge of parameters in terms. By means of the entropy inequality, we simplify the proof of Zellner （1984） and get the uniqueness for the maximal data information prior. A generalized ma- ximal entropy prior is proposed, which improves the olassical maximal entropy principle in some respects. An intermediate solution for the maximal relative prior is developed, from which the maximal relative prior densities for a great number of distribution families are presented.