Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example
Abstract
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.
Cite
@article{arxiv.1301.3880,
title = {Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example},
author = {Thomas D. Nielsen and Pierre-Henri Wuillemin and Finn Verner Jensen and Uffe Kjærulff},
journal= {arXiv preprint arXiv:1301.3880},
year = {2013}
}
Comments
Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)