Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains

2013/02/20 by Heckerman, David, Geiger, Dan · 2 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #G.3 #I.2

paper · doi:10.48550/arxiv.1302.4957

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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