Belief Evolution Network: Probability Transformation of Basic Belief
  Assignment and Fusion Conflict Probability
  We give a new interpretation of basic belief assignment transformation into
probability distribution, and use directed acyclic network called belief
evolution network to describe the causality between the focal elements of a
BBA. On this basis, a new probability transformations method called full
causality probability transformation is proposed, and this method is superior
to all previous method after verification from the process and the result. In
addition, using this method combined with disjunctive combination rule, we
propose a new probabilistic combination rule called disjunctive transformation
combination rule. It has an excellent ability to merge conflicts and an
interesting pseudo-Matthew effect, which offer a new idea to information fusion
besides the combination rule of Dempster.
