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dc.contributor.authorSander, Jennifer
dc.date.accessioned2021-05-27T09:28:33Z
dc.date.available2021-05-27T09:28:33Z
dc.date.issued2021
dc.identifierONIX_20210527_9783731510628_34
dc.identifier.issn1614-3914
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/48837
dc.languageGerman
dc.relation.ispartofseriesKarlsruher Schriften zur Anthropomatik
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.classificationthema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientistsen_US
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.titleAnsätze zur lokalen Bayes’schen Fusion von Informationsbeiträgen heterogener Quellen
dc.typebook
oapen.abstract.otherlanguageThe 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.
oapen.identifier.doi10.5445/KSP/1000125447
oapen.relation.isPublishedBy44e29711-8d53-496b-85cc-3d10c9469be9
oapen.relation.isbn9783731510628
oapen.series.number45
oapen.pages342
oapen.place.publicationKarlsruhe


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