A Comparison of Evidential Networks and Compositional Models

0439574 - ÚTIA 2015 RIV CZ eng J - Journal Article
Vejnarová, Jiřina
A Comparison of Evidential Networks and Compositional Models.
Kybernetika. Roč. 50, č. 2 (2014), s. 246-267. ISSN 0023-5954
R&D Projects: GA ČR GA13-20012S
Institutional support: RVO:67985556
Keywords : evidence theory * graphical models * conditional independence
Subject RIV: BA - General Mathematics
Impact factor: 0.541, year: 2014
http://library.utia.cas.cz/separaty/2014/MTR/vejnarova-0439574.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0242968