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

"Ideal Parent" Structure Learning for Continuous Variable Networks