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Embedding the results of focussed Bayesian fusion into a global context

 
: Sander, Jennifer; Heizmann, Michael

:
Postprint urn:nbn:de:0011-n-3196990 (213 KByte PDF)
MD5 Fingerprint: 31df9449f7d9b45ab8e89900716ab763
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 28.5.2019


Braun, J. J. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Multisensor, multisource information fusion: architectures, algorithms, and applications 2014. Proceedings : Part of SPIE DSS Sensing Technology + Applications, 6-7 May 2014, Baltimore, Maryland, United States
Bellingham, WA: SPIE, 2014 (Proceedings of SPIE 9121)
ISBN: 978-1-62841-058-7
Paper 912103, 15 S.
Conference "Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications" <2014, Baltimore/Md.>
Conference on Sensing Technology and Applications (DSS) <2014, Baltimore/Md.>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Abstract
Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.

: http://publica.fraunhofer.de/dokumente/N-319699.html