Probabilistic graphical models and their application in data fusion

Steven Bottone; Clay Stanek

doi:10.1117/12.722568

7 May 2007 Probabilistic graphical models and their application in data fusion

Steven Bottone, Clay Stanek

Proceedings Volume 6566, Automatic Target Recognition XVII; 65660L (2007) https://doi.org/10.1117/12.722568
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

Steven Bottone and Clay Stanek "Probabilistic graphical models and their application in data fusion", Proc. SPIE 6566, Automatic Target Recognition XVII, 65660L (7 May 2007); https://doi.org/10.1117/12.722568

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