We present a Dempster-Shafer (DS) approach to finding confidence bounds on the mass of the Higgs boson. 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 incorporation ...

The conditioning in the Dempster-Shafer Theory of Evidence has been defined (by Shafer [15] as combination of a belief function and of an ”event” via Dempster rule. On the other hand Shafer [15] gives a ”probabilistic” interpretation of a belief function (hence indirectly its derivation from a sample). Given the fact that conditional probability distribution of a sample-derived probability dist...

Context-sensing for context-aware HCI challenges the traditional sensor fusion methods with dynamic sensor configuration and measurement requirements commensurate with human perception. The Dempster-Shafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. Our Sensor Fusion for Contextaware Computing Project aims to build a generaliz...

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...

Abstract In view of the lack effective information fusion model for heterogeneous multi-sensor, an improved Dempster/Shafer (DS) evidence theory algorithm is designed to fuse multi-sensor information. The first introduces compatibility coefficient characterize between evidence, obtains weight matrix each proposition, and then redistributes basic probability distribution focal element obtain a n...

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...

We formulate Dempster Shafer Belief functions in terms of Propositional Logic, using the im­ plicit notion of provability underlying Demp­ ster Shafer Theory. The assignment of weights to the propositional literals enables the Belief functions to be explicitly computed using Net­ work Reliability techniques. Also, the updat­ ing of Belief functions using Dempster's Rule of Combination correspon...

The method of reasoning with uncertain information known as Dempster-Shafer theory arose from the reinterpretation and development of work of Arthur Dempster [Dempster, 67; 68] by Glenn Shafer in his book a mathematical theory of evidence [Shafer, 76], and further publications e.g., [Shafer, 81; 90]. More recent variants of Dempster-Shafer theory include the Transferable Belief Model see e.g., ...