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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.
doi:10.1016/0888-613X(96)00113-2 
Copyright © 1996 Published by Elsevier Science Inc.
Reasoning in evidential networks with conditional belief functions
Available online 12 February 1999.
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Abstract
In the existing evidential networks applicable to belief functions, the relations among the variables are always represented by joint belief functions on the product space of the variables involved. In this paper, we use conditional belief functions to represent such relations in the network and show some relations between these two kinds of representations. We also present a propagation algorithm for such networks. By analyzing the properties of some special networks with conditional belief functions, called networks with partial dependency, we show that the computation for reasoning can be simplified.
