Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example

When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks deterministic part of the model can be represented as a Boolean function, and a central part of belief updating reduces to the...

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Bibliographic Details
Published in:arXiv.org 2013-01
Main Authors: Nielsen, Thomas D, Pierre-Henri Wuillemin, Finn Verner Jensen, Kjærulff, Uffe
Format: Article
Language:English
Subjects:
Bayesian analysis
Boolean algebra
Boolean functions
Networks
Troubleshooting
Online Access:Get full text
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Summary:When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks deterministic part of the model can be represented as a Boolean function, and a central part of belief updating reduces to the task of calculating the number of satisfying configurations in a Boolean function. In this paper we explore how advances in the calculation of Boolean functions can be adopted for belief updating, in particular within the context of troubleshooting. We present experimental results indicating a substantial speed-up compared to traditional junction tree propagation.
ISSN:2331-8422