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Journal of Machine Learning Research 7 (2006) 191246 Submitted 3/05; Revised 9/05; Published 2/06 Learning the Structure of
 

Summary: Journal of Machine Learning Research 7 (2006) 191246 Submitted 3/05; Revised 9/05; Published 2/06
Learning the Structure of
Linear Latent Variable Models
Ricardo Silva RBAS@GATSBY.UCL.AC.UK
Gatsby Computational Neuroscience Unit
University College London
London, WC1N 3AR, UK
Richard Scheines SCHEINES@ANDREW.CMU.EDU
Clark Glymour CG09@ANDREW.CMU.EDU
Peter Spirtes PS7Z@ANDREW.CMU.EDU
Center for Automated Learning and Discovery (CALD) and Department of Philosophy
Carnegie Mellon University
Pittsburgh, PA 15213, USA
Editor: David Maxwell Chickering
Abstract
We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which
the members of each subset are d-separated by a single common unrecorded cause, if such exists;
(2) return information about the causal relations among the latent factors so identified. We prove
the procedure is point-wise consistent assuming (a) the causal relations can be represented by a
directed acyclic graph (DAG) satisfying the Markov Assumption and the Faithfulness Assumption;

  

Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University
Scheines, Richard - School of Computer Science & Department of Philosophy, Carnegie Mellon University

 

Collections: Computer Technologies and Information Sciences; Mathematics; Multidisciplinary Databases and Resources