A new probabilistic transformation of belief mass assignment
  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.
