Evidential Reasoning with Conditional Belief Functions
This work addresses a methodological improvement for researchers in evidential reasoning and uncertainty modeling, but it appears incremental as it builds on existing frameworks.
The paper tackles the problem of representing relations in evidential networks by using conditional belief functions instead of joint belief functions, and it presents a propagation algorithm that simplifies the reasoning process in such networks.
In the existing evidential networks with belief functions, the relations among the variables are always represented by joint belief functions on the product space of the involved variables. In this paper, we use conditional belief functions to represent such relations in the network and show some relations of these two kinds of representations. We also present a propagation algorithm for such networks. By analyzing the properties of some special evidential networks with conditional belief functions, we show that the reasoning process can be simplified in such kinds of networks.