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Factoring Dynamic Bayesian Networks based on structural observability

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3 Author(s)
Indranil Roychoudhury ; SGT, Inc. at NASA Ames Research Center, Moffett Field, CA 94035, USA ; Gautam Biswas ; Xenofon Koutsoukos

Dynamic Bayesian networks (DBNs) provide a systematic framework for robust online monitoring of dynamic systems. This paper presents an approach for increasing the efficiency of online estimation by partitioning a system DBN into a set of smaller factors, such that estimation algorithms can be applied to each factor independently. Our factoring scheme is based on the analysis of structural observability of the dynamic system. We establish the theoretical background for structural observability and derive an algorithm for generating the factors using structural observability analysis. We present experimental results to demonstrate the effectiveness of our factoring approach for accurate estimation of system behavior.

Published in:

Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on

Date of Conference:

15-18 Dec. 2009