Learning Gaussian Networks

Understand

We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data.

• The algorithms have two components: a scoring metric and a search procedure.
• The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to the posterior probability of the network structure given the data.
• The search procedure generates networks for evaluation by the scoring metric.