The IMAP hybrid method for learning Gaussian bayes nets

Schulte, Oliver, Frigo, Gustavo, Greiner, Russell and Khosravi, Hassan (2010). The IMAP hybrid method for learning Gaussian bayes nets. In: Advances in Artificial Intelligence - 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Proceedings. 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, ON, Canada, (123-134). 31 May - 2 June 2010. doi:10.1007/978-3-642-13059-5_14


Author Schulte, Oliver
Frigo, Gustavo
Greiner, Russell
Khosravi, Hassan
Title of paper The IMAP hybrid method for learning Gaussian bayes nets
Conference name 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010
Conference location Ottawa, ON, Canada
Conference dates 31 May - 2 June 2010
Proceedings title Advances in Artificial Intelligence - 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Proceedings   Check publisher's open access policy
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Place of Publication Berlin, Germany
Publisher Springer
Publication Year 2010
Sub-type Fully published paper
DOI 10.1007/978-3-642-13059-5_14
ISBN 3642130585
ISSN 0302-9743
Volume 6085
Start page 123
End page 134
Total pages 12
Abstract/Summary This paper presents the I-map hybrid algorithm for selecting, given a data sample, a linear Gaussian model whose structure is a directed graph. The algorithm performs a local search for a model that meets the following criteria: (1) The Markov blankets in the model should be consistent with dependency information from statistical tests. (2) Minimize the number of edges subject to the first constraint. (3) Maximize a given score function subject to the first two constraints. Our local search is based on Graph Equivalence Search (GES); we also apply the recently developed SIN statistical testing strategy to help avoid local minima. Simulation studies with GES search and the BIC score provide evidence that for nets with 10 or more variables, the hybrid method selects simpler graphs whose structure is closer to the target graph.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Q-Index Code E1
Institutional Status Non-UQ

Document type: Conference Paper
Collection: Institute for Teaching and Learning Innovation Publications
 
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Created: Thu, 15 Sep 2016, 02:27:38 EST by Hassan Khosravi on behalf of Learning and Research Services (UQ Library)