The Importance of Statistical Evidence for Focussed Bayesian Fusion

Conference paper


Jennifer Sander
Jonas Krieger
Jürgen Beyerer


Rüdiger Dillmann, Jürgen Beyerer, Uwe D. Hanebeck, Tanja Schultz (eds.), KI 2010: Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence, vol. 6359, Springer, 2010.




KI 2010 - 33rd Annual German Conference on Artificial Intelligence, Karlsruhe, September 21 - 24, 2010

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.