Identifying the Relevant Nodes Without Learning the Model
Machine Learning
2012-07-02 v1 Artificial Intelligence
Machine Learning
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
@article{arxiv.1206.6847,
title = {Identifying the Relevant Nodes Without Learning the Model},
author = {Jose M. Pena and Roland Nilsson and Johan Björkegren and Jesper Tegnér},
journal= {arXiv preprint arXiv:1206.6847},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI2006)