Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains

David Heckerman; Dan Geiger

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arXiv · 1302.4957

Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains

David Heckerman·Dan Geiger

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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.

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David Heckerman, Dan Geiger. 2013-02-20. Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains. https://arxiv.org/abs/1302.4957

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