Uncertainty in Artificial Intelligence
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Cross-covariance modelling via DAGs with hidden variables
Jacob Wegelin, Thomas Richardson
Abstract:
DAG models with hidden variables present many difficulties that are not present when all nodes are observed. In particular, fully observed DAG models are identified and correspond to well-defined sets ofdistributions, whereas this is not true if nodes are unobserved. Inthis paper we characterize exactly the set of distributions given by a class of one-dimensional Gaussian latent variable models. These models relate two blocks of observed variables, modeling only the cross-covariance matrix. We describe the relation of this model to the singular value decomposition of the cross-covariance matrix. We show that, although the model is underidentified, useful information may be extracted. We further consider an alternative parametrization in which one latent variable is associated with each block. Our analysis leads to some novel covariance equivalence results for Gaussian hidden variable models.
Keywords: DAG, latent variable, hidden variable, SVD, singular value decomposition, behavioral
Pages: 546-553
PS Link: http://www.stat.washington.edu/wegelin/PAPERS/2001-08-04uaiPaper.ps
PDF Link: /papers/01/p546-wegelin.pdf
BibTex:
@INPROCEEDINGS{Wegelin01,
AUTHOR = "Jacob Wegelin and Thomas Richardson",
TITLE = "Cross-covariance modelling via DAGs with hidden variables",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "546--553"
}


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