NDLI: On the uncertainty and ignorance of statistical decision and evidence combination

Content Provider	IEEE Xplore Digital Library
Author	Xiao-Gang Wang Wen-Han Qian Pagello, E. Ren-Qing Pei
Copyright Year	1996
Description	Author affiliation: Coll. of Mech. Eng. & Autom., Shanghai Univ., China (Xiao-Gang Wang)
Abstract	The classical Bayesian decision theory and the hypothesis testing for processing distributed decision fusion problems have an important shortcoming-lack of flexibility. In other words, they can not discriminate uncertainty and ignorance. The Dempster-Shafer (DS) theory overcomes this shortcoming, but its mathematical basis, the axiomatic definition of evidence is not very rigorous. Therefore, a perfect, reliable, and general method of statistical decision and evidence combination is demanded. In this respect, Thomopoulos presented a generalized evidence processing (GEP) method, based on Bayesian theory and DS theory. This paper presents a new strategy for statistical decision and evidence combination-the double bound testing (DBT). Compared with GEP, DBT not only increases the flexibility of decision, but also presents a sound mathematical basis and an explicit concept.
Starting Page	166
Ending Page	173
File Size	573678
Page Count	8
File Format	PDF
ISBN	078033700X
DOI	10.1109/MFI.1996.572174
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Bayesian methods Military aircraft Aerospace electronics Intelligent sensors Sensor fusion Robotics and automation Decision theory Electronic equipment testing Radar detection
Content Type	Text
Resource Type	Article

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