A new probabilistic transformation of belief mass assignment

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dc.creatorDezert, Jean
dc.creatorSmarandache, Florentin
dc.date2008-07-23
dc.date.accessioned2026-07-07T09:52:22Z
dc.date.available2026-07-07T09:52:22Z
dc.descriptionIn 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.
dc.identifierhttps://arxiv.org/abs/0807.3669
dc.identifierhttp://arxiv.org/abs/0807.3669
dc.identifierFusion 2008 International Conference on Information Fusion, Cologne : Allemagne (2008)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/165572
dc.subjectArtificial Intelligence
dc.titleA new probabilistic transformation of belief mass assignment
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