نتایج جستجو برای: dempster shafer theory of evidence

تعداد نتایج: 21289397  

1999
Malcolm Beynon Bruce Curry Peter Morgan

The objective of this paper is to describe the potential o€ered by the Dempster±Shafer theory (DST) of evidence as a promising improvement on ``traditional'' approaches to decision analysis. Dempster±Shafer techniques originated in the work of Dempster on the use of probabilities with upper and lower bounds. They have subsequently been popularised in the literature on Arti®cial Intelligence (AI...

2010
Simon Parsons E. H. Mamdani Philippe Smets

This paper introduces the idea of using the Dempster-Shafer theory of evidence with qualitative values. Dempster-Shafer theory is a formalism for reasoning under uncertainty which may be viewed as a generalisation of probability theory with special advantages in its treatment of ambiguous data and the ignorance arising from it. Here we are interested in applying the theory when the numbers that...

1993
James C. Ho

This paper presents an applied practical comparison of Bayesian and Dempster-Shafer techniques useful for managing uncertainty in sensing. Three formulations of the same example are presented: a Bayesian, a naive Dempster-Shafer, and a Dempster-Shafer approach using a reened frame of discernment. Both the Bayesian and Dempster-Shafer (with a reened frame of discernment) yield similar results; h...

Journal: :CoRR 2014
Meizhu Li Qi Zhang Xinyang Deng Yong Deng

Dempster-Shafer theory is widely applied in uncertainty modelling and knowledge reasoning due to its ability of expressing uncertain information. A distance between two basic probability assignments(BPAs) presents a measure of performance for identification algorithms based on the evidential theory of Dempster-Shafer. However, some conditions lead to limitations in practical application for Dem...

1994
Simon Parsons Philippe Smets

This paper introduces the idea of using the Dempster-Shafer theory of evidence with qualitative values. Dempster-Shafer theory is a formalism for reasoning under uncertainty which may be viewed as a generalisation of probability theory with special advantages in its treatment of ambiguous data and the ignorance arising from it. Here we are interested in applying the theory when the numbers that...

1993
Simon Parsons E. H. Mamdani Philippe Smets

This paper introduces the idea of using the Dempster-Shafer theory of evidence with qualitative values. Dempster-Shafer theory is a formalism for reasoning under uncertainty which may be viewed as a generalisation of probability theory with special advantages in its treatment of ambiguous data and the ignorance arising from it. Here we are interested in applying the theory when the numbers that...

2016
Xin Wang Ying Wang Hongbin Sun

In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among i...

2011
Radim Jirousek

This paper is one of many attempts to introduce graphical Markov models within Dempster-Shafer theory of evidence. Here we take full advantage of the notion of factorization, which in probability theory (almost) coincides with the notion of conditional independence. In Dempster-Shafer theory this notion can be quite easily introduced with the help of the operator of composition. Nevertheless, t...

2013
Andino Maseleno Md. Mahmud Hasan

This paper presents Dempster-Shafer Theory for insect diseases detection. Sustainable elimination of insect diseases as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we used Dempster-Shafer theory for detecting insect diseases and displaying the result of detection process. Insect diseases which include babesiosis, dengue fever,...

Journal: :International Journal of Approximate Reasoning 2016

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