Abstract
The term “Bayesification” is introduced as meaning the adoption of criteria, that keep the Bayes type of solution as far as the knowledge of the probability distribution over the states of nature extends. A general decision model in Wald's sense is developed, Bayes and minimax solution are defined and reasons are given for the Bayesification. Neither the Bayes nor the minimax criterion per se allow Bayesification. In order to get a Bayesification in the above sense the Bayes type of solution is extended to a certain combination of Bayes and minimax type of solution. This extension is generalized in order to provide a mechanism that allows the decision maker to incorporate into the decision criterion any a priori information available. It is shown that special cases of the generalized criterion are Bayes solution, minimax solution, and a solution analogous to that ofHodges andLehmann. Another special case of the generalized criterion is shown as a special case of a criterion proposed bySchneeweiss.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Unternehmensforschung Operations Research - Recherche Opérationnelle
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
