In some information fusion processes, the incomplete information set can be naturally mapped into a belief theory information set and a Bayesian probability theory information set. For decision making, the mapping of the belief theory fusion results represented by the basic belief assignment to a probability set is accomplished via a pignistic probability transform. This article introduces the inverse pignistic probability transforms (IPPT) that map the posteriori probabilities into the belief function theories, basic belief assignments. Also introduced are two infinite classes and some finite classes of mapping the posteriori probability results to the basic belief assignment of the belief theory.


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    Title :

    Inverse pignistic probability transforms


    Contributors:


    Publication date :

    2002-01-01


    Size :

    269539 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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