Article

"Ideal Parent" structure learning for continuous variable networks

Authors:

Iftach Nachman,

Gal Elidan, and

Nir FriedmanAuthors Info & Claims

UAI '04: Proceedings of the 20th conference on Uncertainty in artificial intelligence

July 2004

Pages 400 - 409

Published: 07 July 2004 Publication History

Abstract

In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive procedure, which limits most applications to parameter learning. This problem is even more acute when learning networks with hidden variables. We present a general method for significantly speeding the structure search algorithm for continuous variable networks with common parametric distributions. Importantly, our method facilitates the addition of new hidden variables into the network structure efficiently. We demonstrate the method on several data sets, both for learning structure on fully observable data, and for introducing new hidden variables during structure search.

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Cited By

Lao NZhu JLiu LLiu YCohen W(2010)Efficient relational learning with hidden variable detectionProceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 110.5555/2997189.2997327(1234-1242)Online publication date: 6-Dec-2010
https://dl.acm.org/doi/10.5555/2997189.2997327
Silva RScheines RDzeroski SDe Raedt LWrobel S(2005)New d-separation identification results for learning continuous latent variable modelsProceedings of the 22nd international conference on Machine learning10.1145/1102351.1102453(808-815)Online publication date: 7-Aug-2005
https://dl.acm.org/doi/10.1145/1102351.1102453

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Information & Contributors

Information

Published In

cover image ACM Other conferences

UAI '04: Proceedings of the 20th conference on Uncertainty in artificial intelligence

July 2004

657 pages

ISBN:0974903906

Conference Chair:
Christopher Meek
Microsoft Research
,
Program Chairs:
Max Chickering
Microsoft Reasearch
,
Joseph Halpern
Cornell University

Sponsors

Alberta Ingenuity Centre for Machine Learning
Sun Microsystems of Canada
Hewlett-Packard Laboratories
Information Extraction and Transportation
Informatics Circle of Research Excellence
Yahoo! Research Labs
IBMR: IBM Research
Intel: Intel
Microsoft Research: Microsoft Research
Pacific Institute of Mathematical Sciences
Boeing
University of Alberta: University of Alberta
Northrop Grumman Corporation

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AUAI Press

Arlington, Virginia, United States

Publication History

Published: 07 July 2004

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Conference

UAI '04

Sponsor:

IBMR
Intel
Microsoft Research
University of Alberta

UAI '04: Uncertainty in artificial intelligence

July 7 - 11, 2004

Banff, Canada

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Cited By

Lao NZhu JLiu LLiu YCohen W(2010)Efficient relational learning with hidden variable detectionProceedings of the 23rd International Conference on Neural Information Processing Systems - Volume 110.5555/2997189.2997327(1234-1242)Online publication date: 6-Dec-2010
https://dl.acm.org/doi/10.5555/2997189.2997327
Silva RScheines RDzeroski SDe Raedt LWrobel S(2005)New d-separation identification results for learning continuous latent variable modelsProceedings of the 22nd international conference on Machine learning10.1145/1102351.1102453(808-815)Online publication date: 7-Aug-2005
https://dl.acm.org/doi/10.1145/1102351.1102453

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