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The importance of statistical evidence for focussed bayesian fusion

: Sander, J.; Krieger, J.; Beyerer, J.

Preprint urn:nbn:de:0011-n-1415241 (134 KByte PDF)
MD5 Fingerprint: 59512e4349525cbca7f420ac0355f28b
The original publication is available at
Erstellt am: 7.7.2012

Dillmann, R.; Beyerer, J.; Aziz, Z.:
KI 2010: Advances in artificial intelligence. 33rd Annual German Conference on AI : Karlsruhe, Germany, September 21 -24, 2010, proceedings
Berlin: Springer, 2010 (Lecture Notes in Artificial Intelligence 6359)
ISBN: 978-3-642-16110-0
ISBN: 3-642-16110-3
ISSN: 0302-9743
German Conference on Artificial Intelligence (KI) <33, 2010, Karlsruhe>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

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.