Ansätze zur lokalen Bayes’schen Fusion von Informationsbeiträgen heterogener Quellen

• Autore: Sander Jennifer
• Pubblicazione: Karlsruhe, : KIT Scientific Publishing, 2021
• Descrizione fisica: 1 electronic resource (342 p.)
• Soggetto topico: Maths for computer scientists
• Soggetto non controllato: Informationsfusion; heterogene Informationsquellen; Bayes’sche Theorie; Prinzip der Maximalen Entropie; Unsicherheit; information fusion; heterogeneous information sources; Bayesian theory; Maximum Entropy principle; uncertainty
• Sommario/riassunto: 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.
• Titolo autorizzato: Ansätze zur lokalen Bayes’schen Fusion von Informationsbeiträgen heterogener Quellen
• ISBN: 1000125447
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Tedesco
• Record Nr.: 9910476899203321
• Lo trovi qui: Univ. Federico II