We present a Dempster–Shafer (DS) approach to estimating limits from Poisson counting data with nuisance parameters. Dempster– Shafer is a statistical framework that generalizes Bayesian statistics. DS calculus augments traditional probability by allowing mass to be distributed over power sets of the event space. This eliminates the Bayesian dependence on prior distributions while allowing the ...

The framework for decision value oriented decomposition of data tables is stated with examples of its applications to partially generalized reasoning. Operation of synthesis of information is introduced for distributed decision tables. Theoretical foundations are built on the basis of the main factors of quality of reasoning, by referring to rough set, Dempster-Shafer and statistical theories.

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...

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,...

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 its development and application to a large extend. To overcome these shortcomings in Dempster-Shafer theory and enhance its capability of representing uncert...

We provide an approach to generalized metrics that covers various concepts of distance. In particular, we consider functorial maps which are weakly positive. Here, we focus on the supermodular case which generalizes dimension functions. We give a lattice-theoretically based construction for supermodular functorial maps, which generalize those arising from Dempster-Shafer-Theory. Within this fra...

This paper responds to a number of criticisms of Dempster-Shafer theory made by Judea Pearl. He criticises Dempster-Shafer belief for not obeying the laws of Bayesian belief," however, these laws lead to well-known problems in the face of ignorance, and seem unreasonably restrictive. It is argued that it is not reasonable to expect a measure of belief to obey Pearl's sandwich principle. The sta...

The Dempster-Shafer theory of evidence accumulation is one of the main tools for combining data obtained from multiple sources. In this paper a special case of combination of two bodies of evidence with non-zero conflict coefficient is considered. It is shown that application of the Dempster-Shafer rule of combination in this case leads to an evaluation of masses of the combined bodies that is ...

An algorithm for updating the evidence in the Dempster–Shafer theory is presented. The algorithm is based on an idea of indices. These indices are used to code the process of reasoning under uncertainty (the combination of evidence)using the Dempster-Shafer theory. The algorithm allows to carry out the reasoning with updating the evidence in much more efficient way than using the original Demps...

Brief introductions to both Dempster-Shafer and DSm theories are presented. Classical belief conditioning is recalled and generalized to DSm hyper-power sets. Relation of generalization of classic conditioning rules to belief conditioning defined in DSmT is discussed. c ©2008 World Academic Press, UK. All rights reserved.