Some Practical Issues in Modeling Diagnostic Systems with Multiply Sectioned Bayesian Networks

Y. Xiang, University of Regina; K. G. Olesen and F. V. Jensen, Aalborg University

Multiply Sectioned Bayesian Networks (MSBNs) provide a distributed framework for diagnosis of large systems based on probabilistic knowledge. To ensure exact inference, the partition of a large system into subsystems and the representation of subsystems must follow a set of technical constraints. How to satisfy these goals for a given system may not be obvious to a practitioner. In this paper, we address three practical modeling issues.

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