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A Comparison of Evidential Networks and Compositional Models
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SYSNO ASEP 0439574 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title A Comparison of Evidential Networks and Compositional Models Author(s) Vejnarová, Jiřina (UTIA-B) RID Source Title Kybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i. - ISSN 0023-5954
Roč. 50, č. 2 (2014), s. 246-267Number of pages 22 s. Publication form Print - P Language eng - English Country CZ - Czech Republic Keywords evidence theory ; graphical models ; conditional independence Subject RIV BA - General Mathematics R&D Projects GA13-20012S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 UT WOS 000337929500006 EID SCOPUS 84901434883 DOI 10.14736/kyb-2014-2-0246 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2015
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