  When modeling a probability distribution with a Bayesian network, we are
faced with the problem of how to handle continuous variables. Most previous
work has either solved the problem by discretizing, or assumed that the data
are generated by a single Gaussian. In this paper we abandon the normality
assumption and instead use statistical methods for nonparametric density
estimation. For a naive Bayesian classifier, we present experimental results on
a variety of natural and artificial domains, comparing two methods of density
estimation: assuming normality and modeling each conditional distribution with
a single Gaussian; and using nonparametric kernel density estimation. We
observe large reductions in error on several natural and artificial data sets,
which suggests that kernel estimation is a useful tool for learning Bayesian
models.
