Uncertainty in Artificial Intelligence
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A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence
Mathias Drton, Thomas Richardson
Abstract:
Graphical models with bi-directed edges (<->) represent marginal independence: the absence of an edge between two vertices indicates that the corresponding variables are marginally independent. In this paper, we consider maximum likelihood estimation in the case of continuous variables with a Gaussian joint distribution, sometimes termed a covariance graph model. We present a new fitting algorithm which exploits standard regression techniques and establish its convergence properties. Moreover, we contrast our procedure to existing estimation methods
Keywords:
Pages: 184-191
PS Link:
PDF Link: /papers/03/p184-drton.pdf
BibTex:
@INPROCEEDINGS{Drton03,
AUTHOR = "Mathias Drton and Thomas Richardson",
TITLE = "A New Algorithm for Maximum Likelihood Estimation in Gaussian Graphical Models for Marginal Independence",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "184--191"
}


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