Transformations of belief masses into subjective probabilities

In this chapter, we propose in the 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. This theoretical work must increase the performances of DSmT-based hard-decision based systems as well as in soft-decision based systems in many fields where it could be used, i.e. in biometrics, medicine, robotics, surveillance and threat assessment, multisensor-multitarget tracking for military and civilian applications, etc.