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# Learning Bayesian networks with mixed variables

*Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics*, PMLR R3:13-20, 2001.

#### 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.