Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem

AbstractWe generalize the Bayes' theorem within the transferable belief model framework. The generalized Bayesian theorem (GBT) allows us to compute the belief over a space θ given an observation x ⊆ X when one knows only the beliefs over X for every θi ∈ Θ. We also discuss the disjunctive rule of combination (DRC) for distinct pieces of evidence. This rule allows us to compute the belief over X from the beliefs induced by two distinct pieces of evidence when one knows only that one of the pieces of evidence holds. The properties of the DRC and GBT and their uses for belief propagation in directed belief networks are analyzed. The use of the discounting factors is justified. The application of these rules is illustrated by an example of medical diagnosis