Uncertainty in Artificial Intelligence
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Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains
David Heckerman, Dan Geiger
Abstract:
We examine Bayesian methods for learning Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we presented at last year's conference for discrete and Gaussian domains. We derive a general Bayesian scoring metric, appropriate for both domains. We then use this metric in combination with well-known statistical facts about the Dirichlet and normal--Wishart distributions to derive our metrics for discrete and Gaussian domains.
Keywords: Bayesian networks, Gaussian networks, learning, Dirichlet, normal-Wishart, likelihoo
Pages: 274-284
PS Link: http://www.research.microsoft.com/research/dtg/heckerma/TR-95-02.htm
PDF Link: /papers/95/p274-heckerman.pdf
BibTex:
@INPROCEEDINGS{Heckerman95,
AUTHOR = "David Heckerman and Dan Geiger",
TITLE = "Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "274--284"
}


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