Learning Bayesian Networks with Mixed Variables

Abstract

The paper considers conditional Gaussian networks. As conjugate local priors, we use the Dirichlet distribution for discrete variables and the Gaussian-inverse Gamma distribution for continuous variables, given a configuration of the discrete parents. We assume parameter independence and complete data. Further, the network-score is calculated. We then develop a local master prior procedure, for deriving parameter priors in CG networks. The local master procedure satisfies parameter independence, parameter modularity and likelihood equivalence.