DocumentCode :
629536
Title :
Why Dempster´s rule doesn´t behave as Bayes rule with informative priors
Author :
Dezert, Jean ; Tchamova, Albena ; Deqiang Han ; Tacnet, Jean-Marc
Author_Institution :
French Aerosp. Lab., Palaiseau, France
fYear :
2013
fDate :
19-21 June 2013
Firstpage :
1
Lastpage :
5
Abstract :
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. Our analysis proves clearly that Dempster´s rule of combination does not behave as Bayes fusion rule in general, because these methods deal very differently with the prior information when it is really informative (not uniform). Only in the very particular case where the basic belief assignments to combine are Bayesian and when the prior information is uniform (or vacuous), Dempster´s rule remains consistent with Bayes fusion rule. In more general cases, Dempster´s rule is incompatible with Bayes rule and it is not a generalization of Bayes fusion rule.
Keywords :
Bayes methods; belief maintenance; case-based reasoning; sensor fusion; Bayes fusion rule; Dempster fusion rule; Dempster rule of combination; Mathematical Theory of Evidence; belief assignment; belief function; informative priors; Bayes methods; Educational institutions; Mathematical model; Presses; Probabilistic logic; Uncertainty; Upper bound; Bayes fusion rule; Dempster´s fusion rule; Information fusion; Probability theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on
Conference_Location :
Albena
Print_ISBN :
978-1-4799-0659-8
Type :
conf
DOI :
10.1109/INISTA.2013.6577631
Filename :
6577631
Link To Document :
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