Embedded Bayesian Network Classiiers

Low-dimensional probability models for local distribution functions in a Bayesian network include decision trees, decision graphs, and causal independence models. We describe a new probability model for discrete Bayesian networks, which we call an embedded Bayesian network classi er or EBNC. The model for a node Y given parents X is obtained from a (usually di erent) Bayesian network for Y and X in which X need not be the parents of Y . We show that an EBNC is a special case of a softmax polynomial regression model. Also, we show how to identify a non-redundant set of parameters for an EBNC, and describe an asymptotic approximation for learning the structure of Bayesian networks that contain EBNCs. Unlike the decision tree, decision graph, and causal independence models, we are unaware of a semantic justi cation for the use of these models. Experiments are needed to determine whether the models presented in this paper are useful in practice.