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 the 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
Text
Nielsen et al. "Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example." Conference on Uncertainty in Artificial Intelligence, 2000.Markdown
[Nielsen et al. "Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example." Conference on Uncertainty in Artificial Intelligence, 2000.](https://mlanthology.org/uai/2000/nielsen2000uai-using/)BibTeX
@inproceedings{nielsen2000uai-using,
title = {{Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as an Example}},
author = {Nielsen, Thomas D. and Wuillemin, Pierre-Henri and Jensen, Finn Verner and Kjærulff, Uffe},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2000},
pages = {426-435},
url = {https://mlanthology.org/uai/2000/nielsen2000uai-using/}
}