A comparison of evidential networks and compositional models
Jiřina Vejnarová · Kybernetika · 2014
Several counterparts of Bayesian networks based on different paradigms have been proposed in evidence theory.Nevertheless, none of them is completely satisfactory.In this paper we will present a new one, based on a recently introduced concept of conditional independence.We define a conditioning rule for variables, and the relationship between conditional independence and irrelevance is studied with the aim of constructing a Bayesian-network-like model.Then, through a simple example, we will show a problem appearing in this model caused by the use of a conditioning rule.We will also show that this problem can be avoided if undirected or compositional models are used instead.