Efficient Epistemic Updates in Rank-based Belief Networks


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HOHENADEL, Stefan Alexander, 2013. Efficient Epistemic Updates in Rank-based Belief Networks

@phdthesis{Hohenadel2013Effic-25040, title={Efficient Epistemic Updates in Rank-based Belief Networks}, year={2013}, author={Hohenadel, Stefan Alexander}, address={Konstanz}, school={Universität Konstanz} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/25040"> <dc:rights>deposit-license</dc:rights> <dc:creator>Hohenadel, Stefan Alexander</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-11-05T10:11:57Z</dcterms:available> <dc:language>eng</dc:language> <dc:contributor>Hohenadel, Stefan Alexander</dc:contributor> <dcterms:abstract xml:lang="eng">The thesis provides an approach for an efficient update algorithm of rank-based belief networks. The update is performed on two input values: the current doxastic state, represented by the network, and, second, a doxastic evidence that is represented as a change on a subset of the variables in the network. From these inputs, a Lauritzen-Spiegelhalter-styled update strategy can compute the updated posterior doxastic state of the network. The posterior state reflects the combination of the evidence and the prior state. This strategy is well-known for Bayesian networks. The thesis transfers the strategy to those networks whose semantics is specified by epistemic ranking functions instead of probability measures. As a foundation, the construction of rank-based belief networks is discussed, which are graphical models for ranking functions. It is shown that global, local and pairwise Markov properties are equivalent in rank-based belief networks and, furthermore, that the Hammersley-Clifford-Theorem holds for such ranking networks. This means that from the equivalence of the Markov properties it follows that a potential representation of the actual ranking function can be derived from the network structure. It is shown how by this property the update strategy of the Lauritzen-Spiegelhalter-algorithm can be transferred to ranking networks. For this purpose, the solution of the two main problems is demonstrated: first, the triangulation of the moralized input network and the decompositon of this triangulation to a clique tree. Then, second, message passing can be performed on this clique tree to incorporate the evidence into the clique tree. The entire approach is in fact a technical description of belief revision.</dcterms:abstract> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/25040"/> <dcterms:issued>2013</dcterms:issued> <dcterms:title>Efficient Epistemic Updates in Rank-based Belief Networks</dcterms:title> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-11-05T10:11:57Z</dc:date> </rdf:Description> </rdf:RDF>

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

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