Factorization of Dempster-Shafer Belief Functions Based on Data
  One important obstacle in applying Dempster-Shafer Theory (DST) is its
relationship to frequencies. In particular, there exist serious difficulties in
finding factorizations of belief functions from data.
  In probability theory factorizations are usually related to notion of
(conditional) independence and their possibility tested accordingly. However,
in DST conditional belief distributions prove to be non-proper belief functions
(that is ones connected with negative "frequencies"). This makes statistical
testing of potential conditional independencies practically impossible, as no
coherent interpretation could be found so far for negative belief function
values.
  In this paper a novel attempt is made to overcome this difficulty. In the
proposal no conditional beliefs are calculated, but instead a new measure F is
introduced within the framework of DST, closely related to conditional
independence, allowing to apply conventional statistical tests for detection of
dependence/independence.
