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Target Identification with Dynamic Hybrid Bayesian Networks
 Proceedings of the EOS/SPIE Symposium on Remote Sensing, Image and Signal Processing for Remote Sensing VI
, 2000
"... The continuous growth of data has created a demand for better data fusion algorithms. In this study we have used a method called Bayesian networks to answer the demand. The reason why Bayesian networks are used in wide range of applications is that modelling with Bayesian networks offers easy and st ..."
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The continuous growth of data has created a demand for better data fusion algorithms. In this study we have used a method called Bayesian networks to answer the demand. The reason why Bayesian networks are used in wide range of applications is that modelling with Bayesian networks offers easy and straightforward representation for combining a priori knowledge with the observations. Another reason for growing use of the Bayesian networks is that Bayesian networks can combine attributes having different dimensions. In addition to the quite wellknown theory of discrete and continuous Bayesian networks, we introduce a reasoning scheme to the hybrid Bayesian networks. The reasoning method used is based on polytree algorithm. Our aim is to show how to apply the hybrid Bayesian networks to identification. Also one method to achieve dynamic features is discussed. We have simulated dynamic hybrid Bayesian networks in order to identify aircraft in noisy environment.
APPROVED FOR PUBLIC RELEASE A Tutorial on Bayesian Belief Networks EXECUTIVE SUMMARY
, 2001
"... This tutorial provides an overview of Bayesian belief networks. The subject is introduced through a discussion on probabilistic models that covers probability language, dependency models, graphical representations of models, and belief networks as a particular representation of probabilistic models. ..."
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This tutorial provides an overview of Bayesian belief networks. The subject is introduced through a discussion on probabilistic models that covers probability language, dependency models, graphical representations of models, and belief networks as a particular representation of probabilistic models. The general class of causal belief networks is presented, and the concept of dseparation and its relationship with independence in probabilistic models is introduced. This leads to a description of Bayesian belief networks as a specific class of causal belief networks, with detailed discussion on belief propagation and practical network design. The target recognition problem is presented as an example of the application of Bayesian belief networks to a real problem, and the tutorial concludes with a brief summary of Bayesian belief networks.