Directed evidential networks with conditional belief functions

Abstract

The main question addressed in this paper is how to represent belief functions independencies by graphical model. Directed evidential networks (DEVNs) with conditional belief functions are then proposed. These networks are directed acyclic graphs (DAGs) similar to Bayesian networks but instead of using probability functions, we use belief functions. Directed evidential network with conditional belief functions has the advantage of providing an appropriate representation of the knowledge that can be produced as conditional relationships.