  In recent years Bayesian networks (BNs) with a mixture of continuous and
discrete variables have received an increasing level of attention. We present
an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG
BNs). The architecture is an extension of lazy propagation using operations of
Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator
potentials into sets of factors, the proposed architecture takes advantage of
independence and irrelevance properties induced by the structure of the graph
and the evidence. The resulting benefits are illustrated by examples. Results
of a preliminary empirical performance evaluation indicate a significant
potential of the proposed architecture.
