Identifying the relevant nodes without learning the model

Jose M. Peña; Roland Nilsson; Johan Björkegren; Jesper Tegner

Identifying the relevant nodes without learning the model

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

^*Corresponding author for this work

Biological, Environmental Sciences and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 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 language	English (US)
Title of host publication	Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Pages	367-374
Number of pages	8
State	Published - Dec 1 2006
Event	22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006 - Cambridge, MA, United States Duration: Jul 13 2006 → Jul 16 2006

Publication series

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

Other

Other	22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006
Country/Territory	United States
City	Cambridge, MA
Period	07/13/06 → 07/16/06

ASJC Scopus subject areas

Artificial Intelligence

Cite this

@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 Tegner",

year = "2006",

month = dec,

day = "1",

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",

}

Peña, JM, Nilsson, R, Björkegren, J & Tegner, J 2006, Identifying the relevant nodes without learning the model. in Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006. Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006, pp. 367-374, 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006, Cambridge, MA, United States, 07/13/06.

Identifying the relevant nodes without learning the model. / Peña, Jose M.; Nilsson, Roland; Björkegren, Johan et al.
Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006. 2006. p. 367-374 (Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identifying the relevant nodes without learning the model

AU - Peña, Jose M.

AU - Nilsson, Roland

AU - Björkegren, Johan

AU - Tegner, Jesper

PY - 2006/12/1

Y1 - 2006/12/1

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:33947511664

SN - 0974903922

SN - 9780974903927

T3 - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

SP - 367

EP - 374

BT - Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

T2 - 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Y2 - 13 July 2006 through 16 July 2006

ER -

Identifying the relevant nodes without learning the model

Abstract

Publication series

Other

ASJC Scopus subject areas

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