A new probabilistic transformation of belief mass assignment

Jean Dezert (1) , Florentin Smarandache (2)

1 ONERA - The French Aerospace Lab [Châtillon]
2 Department of Mathematics and Statistics [Albuquerque]

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.

Mots clés: DSmT; Subjective probability; Probabilistic Information Content; qualitative belief

Domaines: Intelligence artificielle [cs.AI]

Jean Dezert, Florentin Smarandache. A new probabilistic transformation of belief mass assignment. Fusion 2008 International Conference on Information Fusion, Jul 2008, Cologne, Germany. pp.1410-1417. ⟨hal-00304319⟩