Evidential Reasoning with Conditional Belief Functions

TL;DR

This paper introduces a new approach to evidential reasoning using conditional belief functions, simplifying inference in belief networks and providing a propagation algorithm for improved reasoning efficiency.

Contribution

It presents a novel use of conditional belief functions in evidential networks, along with a propagation algorithm and analysis of simplified reasoning processes.

Findings

Conditional belief functions can replace joint belief functions in evidential networks.

The proposed propagation algorithm improves reasoning efficiency.

Special evidential networks with conditional belief functions allow simplified inference.

Abstract

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.