Embedding the results of focussed Bayesian fusion into a global context

Jennifer Sander; Michael Heizmann

doi:10.1117/12.2050323

22 May 2014 Embedding the results of focussed Bayesian fusion into a global context

Jennifer Sander, Michael Heizmann

Proceedings Volume 9121, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014; 912103 (2014) https://doi.org/10.1117/12.2050323
Event: SPIE Sensing Technology + Applications, 2014, Baltimore, MD, United States

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.

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Jennifer Sander and Michael Heizmann "Embedding the results of focussed Bayesian fusion into a global context", Proc. SPIE 9121, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2014, 912103 (22 May 2014); https://doi.org/10.1117/12.2050323

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KEYWORDS

Information fusion

Mathematical modeling

Probability theory

Sensors

Distortion

Monte Carlo methods

Calculus

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