Identifying the relevant nodes without learning the model

Jose M. Peña*, Roland Nilsson, Johan Björkegren, Jesper Tegner

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Pages367-374
Number of pages8
StatePublished - Dec 1 2006
Event22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 - Cambridge, MA, United States
Duration: Jul 13 2006Jul 16 2006

Publication series

NameProceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Other

Other22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
CountryUnited States
CityCambridge, MA
Period07/13/0607/16/06

ASJC Scopus subject areas

  • Artificial Intelligence

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