Institute of Information Theory and Automation

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Publication details

A Comparison of Evidential Networks and Compositional Models

Journal Article

Vejnarová Jiřina


serial: Kybernetika vol.50, 2 (2014), p. 246-267

project(s): GA13-20012S, GA ČR

keywords: evidence theory, graphical models, conditional independence

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abstract (eng):

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.

RIV: BA

2012-12-21 16:10