Information fusion using Bayesian multinets

Peter Bladon; Richard J. Hall

doi:10.1117/12.543661

12 April 2004 Information fusion using Bayesian multinets

Peter Bladon, Richard J. Hall

Proceedings Volume 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004; (2004) https://doi.org/10.1117/12.543661
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Bayesian networks are a powerful and convenient way of encoding expert knowledge. They can be used to infer such "high-level’ variables as "threat’ or "intent’, given observations, background and intelligence data. However, their usefulness depends on the model, i.e. the Bayesian network used for inference. We demonstrate how Bayesian multinets can be used to simplify the representation of certain complex domains, allowing a decomposition into simpler models that are conditionally independent given a class variable. We illustrate this concept using a threat assessment application, in which each component is specialised to a different class of threat and show how this simplifies model construction and target identification.

Citation Download Citation

Peter Bladon and Richard J. Hall "Information fusion using Bayesian multinets", Proc. SPIE 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, (12 April 2004); https://doi.org/10.1117/12.543661

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