A new probabilistic transformation of belief mass assignment

TL;DR

This paper introduces DSmP, a new probabilistic transformation within Dezert-Smarandache Theory, that effectively converts belief assignments into subjective probabilities, outperforming existing methods in information content.

Contribution

The paper presents DSmP, a novel probabilistic transformation applicable to any belief assignment and model, with extensions for qualitative beliefs, improving upon existing transformations.

Findings

DSmP outperforms classical transformations in Probabilistic Information Content.

Demonstrated effectiveness through multiple examples.

Extension to qualitative belief assignments shown to be feasible.

Abstract

In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the DSmP transformation works and we compare it to main existing transformations proposed in the literature so far. We show the advantages of DSmP over classical transformations in term of Probabilistic Information Content (PIC). The direct extension of this transformation for dealing with qualitative belief assignments is also presented.