Bayesian and Information-Theories Priors for Bayesian Network Parameters

We consider Bayesian and information-theoretic approaches for determining non-informative prior distributions in a parametric model family. The information-theoretic approaches are based on the recently modiied deenition of stochastic complexity by Rissanen, and on the Minimum Message Length (MML) approach by Wallace. The Bayesian alternatives include the uniform prior, and the equivalent sample size priors. In order to be able to empirically compare the diierent approaches in practice, the methods are instantiated for a model family of practical importance, the family of Bayesian networks.