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Proceedings Paper

Probabilistic graphical models and their application in data fusion
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Paper Abstract

Probabilistic graphical models, in particular Bayesian networks, provide a consistent framework in which to address problems containing uncertainty and complexity. Probabilistic inference in high-dimensional problems only becomes tractable when the system can be made modular by imposing meaningful conditional independence assumptions. Bayesian networks provide a natural way to accomplish this. As a combination of probability theory and graph theory, the probabilistic aspects of a graphical model provide a consistent way of connecting data to models, while graph theory provides an intuitively appealing interface to express independence assumptions as well as efficient computation algorithms. A detailed example demonstrating various aspects of Bayesian networks for an electronic intelligence (ELINT) sensor data fusion decision system is presented, including a Value of Information (VOI) analysis.

Paper Details

Date Published: 7 May 2007
PDF: 9 pages
Proc. SPIE 6566, Automatic Target Recognition XVII, 65660L (7 May 2007); doi: 10.1117/12.722568
Show Author Affiliations
Steven Bottone, DataPath, Inc. (United States)
Clay Stanek, DataPath, Inc. (United States)

Published in SPIE Proceedings Vol. 6566:
Automatic Target Recognition XVII
Firooz A. Sadjadi, Editor(s)

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