Decision Theoretic Foundations of Graphical Model Selection

This paper describes a decision theoretic for­ mulation of learning the graphical structure of a Bayesian Belief Network from data. This framework subsumes the standard Bayesian approach of choosing the model with the largest posterior probability as the solution of a decision problem with a 0-1 loss func­ tion and allows the use of more general loss functions able to trade-off the complexity of the selected model and the error of choos­ ing an over-simplified model. A new class of loss functions, called disintegrable, is in­ troduced, to allow the decision problem to match the decomposability of the graphical model. With this class of loss functions, the optimal solution to the decision problem can be found using an efficient bottom-up search