Probabilistic graphical models and their application in data fusion
Steven Bottone, Clay J. Stanek · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
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.