A simple Monte-Carlo algorithm can be used to calculate Dempster-Shafer belief very efficiently unless the conflict between the evidences is very high. This paper introduces and explores Markov Chain Monte-Carlo algorithms for calculating Dempster-Shafer belief that can also work well when the conflict is high.

Several approaches of structuring (factorization, decomposition) of Dempster-Shafer joint belief functions from literature are reviewed with special emphasis on their capability to capture independence from the point of view of the claim that belief functions generalize bayes notion of probability. It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets' {Smets:93} directed ac...

Several authors have explained that the like­ lihood ratio measures the strength of the evi­ dence represented by observations in statisti­ cal problems. This idea works fine when the goal is to evaluate the strength of the avail­ able evidence for a simple hypothesis versus another simple hypothesis. However, the ap­ plicability of this idea is limited to simple hypotheses because the likeliho...

We discuss the role of fuzzy sets in modeling business rules. The technology involved in fuzzy systems modeling is described. We next introduce some ideas from the Dempster-Shafer theory of evidence. We use the Dempster-Shafer framework to provide a machinery for including randomness in the fuzzy systems

In this paper, one conflict context reasoning method based on Dempster-Shafer theory is proposed. Firstly the context conflict problems are illustrated and partitioned based on theory of evidence. Then the context model combined with Dempster-Shafer theory is presented and applied to the reasoning method based on Dempster rule of combination. The effectiveness of this method is verified with a ...

In this article we investigate a problem within Dempster±Shafer theory where 2 q À 1 pieces of evidence are clustered into q clusters by minimizing a metacon¯ict function, or equivalently, by minimizing the sum of weight of con¯ict over all clusters. Previously one of us developed a method based on a Hop®eld and Tank model. However, for very large problems we need a method with lower computatio...

Protein sequences classification is an important problem in molecular biology, and it has long been a goal for scientists and researchers. This paper describes an approach to data-driven discovery of sequence motifbased models using neural network classifier based on Dempster-Shafer Theory for assigning protein sequences to functional families. A training set of sequences with unknown functiona...

Recently, Shafer and Srivastava [1], Srivastava and Shafer [2], Srivastava [3]-[4], and Van den Acker [5] have identified appealing features of belief function evidential networks. These networks can express the support that audit evidence provides for assertions, accounts and financial statements. These networks can also aggregate many pieces of evidence into an overall level of support for a ...

Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences. Many approaches based on discounted evidence and weighted average evidence have been investigated and have made significant improvements. Nevert...