@inproceedings{aa83e9ad61064a9ab228f1b23202e8fe,
title = "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, efficient, 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.",
author = "Pe{\~n}a, {Jose M.} and Roland Nilsson and Johan Bj{\"o}rkegren and Jesper Tegn{\'e}r",
year = "2006",
language = "English (US)",
isbn = "0974903922",
series = "Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006",
pages = "367--374",
booktitle = "Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006",
note = "22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 ; Conference date: 13-07-2006 Through 16-07-2006",
}