We examine Bayesian methods for learn ing Bayesian networks from a combination of prior knowledge and statistical data. In particular, we unify the approaches we pre sented at last year's conference for discrete and Gaussian domains. We derive a gen eral 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 dis tributions to derive our metrics for discrete and Gaussian domains.
In REF the authors introduce a unified method for both discrete and continuous domains to learn the parameters of Bayesian Network, using a combination of prior knowledge and statistical data.
Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains
ArXiv
ArXiv
Computer Science