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Bayesian Estimation with Uncertain Parameters of Probability Density Functions

Klumpp, Vesa; Hanebeck, Uwe D.

In this paper, we address the problem of processing imprecisely known probability density func- tions by means of Bayesian estimation. The imprecise knowledge about probability density functions is given as stochastic uncertainty about their parameters. The proposed processing of this special density in a Bayesian estimator is accomplished by reinterpretation of the Fil- ter and prediction equations. Here, the parameters are treated as a higher order state, which can be processed by Bayesian estimation techniques. For state estima- tion, this avoids the need to select specific values for unknown parameters and, thus, allows the processing of all potential parameters at once. The proposed approach further allows the use of imprecisely known model equa- tions for measurement and state prediction by the same principle.

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Volltext §
DOI: 10.5445/IR/1000029059
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2009
Sprache Englisch
Identifikator urn:nbn:de:swb:90-290596
KITopen-ID: 1000029059
Erschienen in Proceedings of the 12th International Conference on Information Fusion (Fusion 2009). Seattle, Washington, USA, 06.- 09.07.2009
Seiten 1759-1766
Nachgewiesen in Scopus
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