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Related papers: The Assumptions Behind Dempster's Rule

200 papers

While belief functions may be seen formally as a generalization of probabilistic distributions, the question of the interactions between belief functions and probability is still an issue in practice. This question is difficult, since the…

Logic in Computer Science · Computer Science 2011-10-03 Frederic Dambreville

Conditioning is crucial in applied science when inference involving time series is involved. Belief calculus is an effective way of handling such inference in the presence of epistemic uncertainty -- unfortunately, different approaches to…

Artificial Intelligence · Computer Science 2021-04-22 Fabio Cuzzolin

We extend the notion of belief function to the case where the underlying structure is no more the Boolean lattice of subsets of some universal set, but any lattice, which we will endow with a minimal set of properties according to our…

Discrete Mathematics · Computer Science 2008-11-21 Michel Grabisch

Efficient modeling of uncertain information in real world is still an open issue. Dempster-Shafer evidence theory is one of the most commonly used methods. However, the Dempster-Shafer evidence theory has the assumption that the hypothesis…

Artificial Intelligence · Computer Science 2014-05-14 Yong Deng

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…

Artificial Intelligence · Computer Science 2008-12-18 Arnaud Martin , Christophe Osswald

By elaborating on the notion of linear belief functions (Dempster 1990; Liu 1996), we propose an elementary approach to knowledge representation for expert systems using linear belief functions. We show how to use basic matrices to…

Artificial Intelligence · Computer Science 2012-12-12 Liping Liu , Catherine Shenoy , Prakash P. Shenoy

In this paper, we generalize the belief function on complex plane from another point of view. We first propose a new concept of complex mass function based on the complex number, called complex basic belief assignment, which is a…

Artificial Intelligence · Computer Science 2019-07-11 Fuyuan Xiao

When we merge information in Dempster-Shafer Theory (DST), we are faced with anomalous behavior: agents with equal expertise and credibility can have their opinion disregarded after resorting to the belief combination rule of this theory.…

Artificial Intelligence · Computer Science 2024-08-20 Francisco Aragão , João Alcântara

The paper presents a novel view of the Dempster-Shafer belief function as a measure of diversity in relational data bases. It is demonstrated that under the interpretation The Dempster rule of evidence combination corresponds to the join…

Artificial Intelligence · Computer Science 2017-04-11 Mieczysław A. Kłopotek , Sławomir T. Wierzchoń

Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restrictions, which limits its flexibility in handling complex…

Artificial Intelligence · Computer Science 2026-05-19 Qianli Zhou , Ye Cui , Zhen Li , Witold Pedrycz , Yong Deng

We revisit Zadeh's notion of "evidence of the second kind" and show that it provides the foundation for a general theory of epistemic random fuzzy sets, which generalizes both the Dempster-Shafer theory of belief functions and possibility…

Artificial Intelligence · Computer Science 2022-02-17 Thierry Denoeux

Choice functions constitute a simple, direct and very general mathematical framework for modelling choice under uncertainty. In particular, they are able to represent the set-valued choices that typically arise from applying decision rules…

Artificial Intelligence · Computer Science 2018-06-05 Jasper De Bock , Gert de Cooman

This paper investigates the issues of combination and normalization of interval-valued belief structures within the framework of Dempster-Shafer theory of evidence. Existing approaches are reviewed and thoroughly analyzed. The advantages…

Artificial Intelligence · Computer Science 2020-11-30 Miao Qin , Yongchuan Tang

The general use of subjective probabilities to model belief has been justified using many axiomatic schemes. For example, ?consistent betting behavior' arguments are well-known. To those not already convinced of the unique fitness and…

Artificial Intelligence · Computer Science 2013-03-25 Paul Snow

Dempster-Shafer theory is widely applied to uncertainty modelling and knowledge reasoning due to its ability of expressing uncertain information. However, some conditions, such as exclusiveness hypothesis and completeness constraint, limit…

Artificial Intelligence · Computer Science 2014-05-13 Xinyang Deng , Yong Deng

Dempster's rule of combination has been the most controversial part of the Dempster-Shafer (D-S) theory. In particular, Zadeh has reached a conjecture on the noncombinability of evidence from a relational model of the D-S theory. In this…

Artificial Intelligence · Computer Science 2013-04-11 John Yen

This chapter defines a new concept and framework for constructing fusion rules for evidences. This framework is based on a referee function, which does a decisional arbitrament conditionally to basic decisions provided by the several…

Artificial Intelligence · Computer Science 2009-03-30 Frederic Dambreville

We propose a simple method for combining together voting rules that performs a run-off between the different winners of each voting rule. We prove that this combinator has several good properties. For instance, even if just one of the base…

Artificial Intelligence · Computer Science 2012-03-15 Nina Narodytska , Toby Walsh , Lirong Xia

This is a working paper summarizing results of an ongoing research project whose aim is to uniquely characterize the uncertainty measure for the Dempster-Shafer Theory. A set of intuitive axiomatic requirements is presented, some of their…

Artificial Intelligence · Computer Science 2013-02-21 David Harmanec

Accepting a proposition means that our confidence in this proposition is strictly greater than the confidence in its negation. This paper investigates the subclass of uncertainty measures, expressing confidence, that capture the idea of…

Artificial Intelligence · Computer Science 2013-02-21 Didier Dubois , Henri Prade