  In recent years, there is a growing interest in learning Bayesian networks
with continuous variables. Learning the structure of such networks is a
computationally expensive procedure, which limits most applications to
parameter learning. This problem is even more acute when learning networks with
hidden variables. We present a general method for significantly speeding the
structure search algorithm for continuous variable networks with common
parametric distributions. Importantly, our method facilitates the addition of
new hidden variables into the network structure efficiently. We demonstrate the
method on several data sets, both for learning structure on fully observable
data, and for introducing new hidden variables during structure search.

Machine Learning;Machine Learning