title: Statistical inference with partial prior information based on a Gauss-type inequality
abstract: Potter and Anderson (1983) have developed a Bayesian decision procedure requiring the specification of a class of prior distributions restricted to have a minimal probability content for a given subset of the parameter space. They do not, however, provide a method for the selection of that subset. We show how a generalization of Gauss' inequality can be used to determine the relevant parameter subset
keyphrases: partial prior information Bayesian decision procedure prior distributions minimal probability content parameter space Gauss inequality prior-to-posterior sensitivity