Deriving And Combining Continuous Possibility Functions in the Framework of Evidential Reasoning

TL;DR

This paper presents a new, computationally efficient method for combining continuous possibility functions within the Dempster-Shafer evidential reasoning framework, enhancing the integration of continuous statistical data.

Contribution

It introduces a novel rule for combining continuous evidence that is simpler and more efficient than Dempster's rule, along with derivations from probability-density functions.

Findings

The proposed combination rule is computationally more efficient.

The method effectively derives possibility functions from probability densities.

The relationship between Dempster's rule and the new rule is analyzed.

Abstract

To develop an approach to utilizing continuous statistical information within the Dempster- Shafer framework, we combine methods proposed by Strat and by Shafero We first derive continuous possibility and mass functions from probability-density functions. Then we propose a rule for combining such evidence that is simpler and more efficiently computed than Dempster's rule. We discuss the relationship between Dempster's rule and our proposed rule for combining evidence over continuous frames.