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Bayesian networks (BNs) represent joint space probabilities compactly and enable one to carry out efficient inferencing. Although the Dempster-Shafer (DS) belief theoretic framework captures a wider class of imperfections, its utility in such graphical models is limited. This is mainly due to the requirement of having to maintain a basic probability assignment (BPA) for the whole power set of propositions...
We present and discuss a mixed conjunctive and disjunctive rule, a generalization of conflict repartition rules, and a combination of these two rules. In the belief functions theory one of the major problem is the conflict repartition enlightened by the famous Zadeh's example. To date, many combination rules have been proposed in order to solve a solution to this problem. Moreover, it can be important...
Today due to the importance and necessity of implementing security systems in homes and buildings with the capability of higher certainty and lower cost, sensor fusion methods as applicable and high performance methods are attracting the researchers' attention. In this paper, the application of Dempster-Shafer evidential theory and the more general and newer one Dezert-Smarandache theory for implementing...
This paper deals with enriched qualitative belief functions for reasoning under uncertainty and for combining information expressed in natural language through linguistic labels. In this work, two possible enrichments (quantitative and/or qualitative) of linguistic labels are considered and operators (addition, multiplication, division, etc) for dealing with them are proposed and explained. We denote...
Data fusion within the evidential reasoning framework is a well established, robust and conservative technique to fuse uncertain information from multiple sensors. A number of fusion methods within this formalism were introduced including Dempster-Shafer theory (DST) fusion, Dezert Samarandche fusion (DSmT), and Smets' transferable belief model (TBM) based fusion. However, the impact of fusion on...
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