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Statistical inference with partial prior information based on a Gauss-type inequality

✍ Scribed by L.M. Meaux; J.W. Seaman Jr.; D.M. Young


Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
318 KB
Volume
35
Category
Article
ISSN
0895-7177

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✦ Synopsis


Potter and Anderson [1]

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.