Evidential Networks from a Different Perspective
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F12%3A00387929" target="_blank" >RIV/67985556:_____/12:00387929 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-33042-1_46" target="_blank" >http://dx.doi.org/10.1007/978-3-642-33042-1_46</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-33042-1_46" target="_blank" >10.1007/978-3-642-33042-1_46</a>
Alternative languages
Result language
angličtina
Original language name
Evidential Networks from a Different Perspective
Original language description
Bayesian networks are, at present, probably the most popular representative of so-called graphical Markov models. Naturally, several attempts to construct an analogy of Bayesian networks have also been made in other frameworks as e.g. in possibility theory, evidence theory or in more general frameworks of valuation-based systems and credal sets. We collect previously obtained results concerning conditioning, conditional independence and irrelevance allowing to define a new type of evidential networks, based on conditional basic assignments. These networks can be seen as a generalization of Bayesian networks, however, they are less powerful than e.g. so-called compositional models, as we demonstrate by a simple example.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BA - General mathematics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP402%2F11%2F0378" target="_blank" >GAP402/11/0378: Aggregation of knowledge and expectations in the models of mathematical economics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Synergies of Soft Computing and Statistics for Intelligent Data Analysis
ISBN
978-3-642-33041-4
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
429-436
Publisher name
Springer
Place of publication
Heidelberg
Event location
Konstanz
Event date
Oct 4, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000312969600046