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dc.contributor.authorSander, Jennifer
dc.date.accessioned2025-03-08T03:21:49Z
dc.date.available2025-03-08T03:21:49Z
dc.date.issued2021
dc.date.submitted2021-05-27T09:28:33Z
dc.identifierONIX_20210527_9783731510628_34
dc.identifier1614-3914
dc.identifierhttps://library.oapen.org/handle/20.500.12657/48837
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/177172
dc.description.abstractThe solution of various tasks benefits from information fusion or even requires it. The Bayesian fusion methodology is clear, well-founded and fulfills the essential requirements for a meaningful methodology also for fusing the contributions of heterogeneous information sources. In many practically relevant tasks, Bayesian methods cause high, often unacceptable effort. In the work, novel approaches to cope with Bayesian fusion in such situations are formulated and investigated.
dc.languageGerman
dc.relation.ispartofseriesKarlsruher Schriften zur Anthropomatik
dc.rightsopen access
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
dc.subject.otherInformationsfusion
dc.subject.otherheterogene Informationsquellen
dc.subject.otherBayes’sche Theorie
dc.subject.otherPrinzip der Maximalen Entropie
dc.subject.otherUnsicherheit
dc.subject.otherinformation fusion
dc.subject.otherheterogeneous information sources
dc.subject.otherBayesian theory
dc.subject.otherMaximum Entropy principle
dc.subject.otheruncertainty
dc.subject.otherthema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
dc.titleAnsätze zur lokalen Bayes’schen Fusion von Informationsbeiträgen heterogener Quellen
dc.typebook
oapen.identifier.doi10.5445/KSP/1000125447
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.relation.isbn9783731510628
oapen.pages342
oapen.place.publicationKarlsruhe
dc.seriesnumber45
dc.abstractotherlanguageThe solution of various tasks benefits from information fusion or even requires it. The Bayesian fusion methodology is clear, well-founded and fulfills the essential requirements for a meaningful methodology also for fusing the contributions of heterogeneous information sources. In many practically relevant tasks, Bayesian methods cause high, often unacceptable effort. In the work, novel approaches to cope with Bayesian fusion in such situations are formulated and investigated.
dc.anonymityAll identities known
dc.peerreviewid51a542ec-eaeb-47c2-861d-6022e981a97a
dc.peerreviewtitleDissertations in Series (Dissertationen in Schriftenreihe)
dc.openreviewNo
dc.responsibilityBooks or series editor
dc.stagePre-publication
dc.reviewtypeFull text
dc.reviewertypeEditorial board member
dc.reviewertypeExternal peer reviewer


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