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The Importance of Statistical Evidence for Focussed Bayesian Fusion
Author(s) -
Jennifer Sander,
Jonas A. Krieger,
Jürgen Beyerer
Publication year - 2010
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
ISBN - 3-642-16110-3
DOI - 10.1007/978-3-642-16111-7_34
Subject(s) - bayesian probability , computer science , bayesian statistics , artificial intelligence , range (aeronautics) , bayesian experimental design , bayesian econometrics , information fusion , task (project management) , bayesian inference , machine learning , materials science , management , economics , composite material
Focussed Bayesian fusion reduces high computational costs caused by Bayesian fusion by restricting the range of the Properties of Interest which specify the structure of the desired information on its most task relevant part. Within this publication, it is concisely explained how Bayesian theory and the theory of statistical evidence can be combined to derive meaningful focussed Bayesian models and to rate the validity of a focussed Bayesian analysis quantitatively. Earlier results with regard to this topic will be further developed and exemplified.

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