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Ansätze zur lokalen Bayes’schen Fusion von Informationsbeiträgen heterogener Quellen

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...

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Gardado en:
Autor Principal: Sander, Jennifer
Formato: Online
Idioma:alemán
Publicado: KIT Scientific Publishing 2021
Subjects:
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
bic Book Industry Communication::U Computing & information technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
Acceso en liña:ONIX_20210527_9783731510628_34
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Summary: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.

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