MODELLING DEPENDENCE IN DEMPSTER-SHAFER THEORY
Paul-André Monney, Moses W. Chan · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2007
Belief functions can only be combined by Dempster's rule when they are based on independent items of evidence. This paper proposes a method for handling the case where there is some probabilistic dependence among the items of evidence. The method relies on compact representations of joint probability distributions on the assumption variables associated with the belief functions. These distributions are then used to compute degrees of support of hypotheses of interest. It is shown that the theory of hints is the appropriate general framework for this method.