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            Ansätze zur lokalen Bayes’schen Fusion von Informationsbeiträgen heterogener Quellen

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            Author(s)
            Sander, Jennifer
            Language
            German
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            Abstract
            The 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.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/177172
            Keywords
            Informationsfusion; heterogene Informationsquellen; Bayes’sche Theorie; Prinzip der Maximalen Entropie; Unsicherheit; information fusion; heterogeneous information sources; Bayesian theory; Maximum Entropy principle; uncertainty; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
            DOI
            10.5445/KSP/1000125447
            ISBN
            9783731510628
            Publisher
            KIT Scientific Publishing
            Publisher website
            http://www.ksp.kit.edu/
            Publication date and place
            Karlsruhe, 2021
            Series
            Karlsruher Schriften zur Anthropomatik,
            Classification
            Maths for computer scientists
            Pages
            342
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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