Share Email Print
cover

Proceedings Paper

Information fusion using Bayesian multinets
Author(s): Peter Bladon; Richard J. Hall
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 12 April 2004
PDF: 12 pages
Proc. SPIE 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, (12 April 2004); doi: 10.1117/12.543661
Show Author Affiliations
Peter Bladon, BAE SYSTEMS (United Kingdom)
Richard J. Hall, BAE SYSTEMS (United Kingdom)


Published in SPIE Proceedings Vol. 5434:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004
Belur V. Dasarathy, Editor(s)

© SPIE. Terms of Use
Back to Top