Identifying the Relevant Nodes Without Learning the Model
Abstract
We propose a method to identify all the nodes that are relevant to compute all the conditional probability distributions for a given set of nodes. Our method is simple, effcient, consistent, and does not require learning a Bayesian network first. Therefore, our method can be applied to high-dimensional databases, e.g. gene expression databases.
Cite
Text
Peña et al. "Identifying the Relevant Nodes Without Learning the Model." Conference on Uncertainty in Artificial Intelligence, 2006.Markdown
[Peña et al. "Identifying the Relevant Nodes Without Learning the Model." Conference on Uncertainty in Artificial Intelligence, 2006.](https://mlanthology.org/uai/2006/pena2006uai-identifying/)BibTeX
@inproceedings{pena2006uai-identifying,
title = {{Identifying the Relevant Nodes Without Learning the Model}},
author = {Peña, José M. and Nilsson, Roland and Björkegren, Johan and Tegnér, Jesper},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2006},
url = {https://mlanthology.org/uai/2006/pena2006uai-identifying/}
}