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Why Dempster’s fusion rule is not a generalization of Bayes fusion rule
Jean Dezert
Albena Tchamova
Deqiang Han
Jean-Marc Tacnet
出版
Infinite Study
, 2012-10-01
主題
Mathematics / Probability & Statistics / Bayesian Analysis
URL
http://books.google.com.hk/books?id=PCxREAAAQBAJ&hl=&source=gbs_api
EBook
SAMPLE
註釋
In this paper, we analyze Bayes fusion rule in details from a fusion standpoint, as well as the emblematic Dempster’s rule of combination introduced by Shafer in his Mathematical Theory of evidence based on belief functions. We propose a new interesting formulation of Bayes rule and point out some of its properties. A deep analysis of the compatibility of Dempster’s fusion rule with Bayes fusion rule is done. We show that Dempster’s rule is compatible with Bayes fusion rule only in the very particular case where the basic belief assignments (bba’s) to combine are Bayesian, and when the prior information is modeled either by a uniform probability measure, or by a vacuous bba. We show clearly that Dempster’s rule becomes incompatible with Bayes rule in the more general case where the prior is truly informative (not uniform, nor vacuous). Consequently, this paper proves that Dempster’s rule is not a generalization of Bayes fusion rule.
