  In this paper we address the problem of discretization in the context of
learning Bayesian networks (BNs) from data containing both continuous and
discrete variables. We describe a new technique for <EM>multivariate</EM>
discretization, whereby each continuous variable is discretized while taking
into account its interaction with the other variables. The technique is based
on the use of a Bayesian scoring metric that scores the discretization policy
for a continuous variable given a BN structure and the observed data. Since the
metric is relative to the BN structure currently being evaluated, the
discretization of a variable needs to be dynamically adjusted as the BN
structure changes.
