Learning Mixtures of Truncated Basis Functions from Data

In this paper we describe a new method for learning hybrid Bayesian network models from data. The method utilizes a kernel density estimator, which is in turn “translated” into a mixture of truncated basis functions-representation using a convex optimization technique. We argue that these estimators approximate the maximum likelihood estimators, and compare our approach to previous attempts at learning hybrid Bayesian networks from data. We conclude that while the present method produces estimators that are slightly poorer than the state of the art (in terms of log likelihood), it is significantly faster.