  When using Bayesian networks for modelling the behavior of man-made
machinery, it usually happens that a large part of the model is deterministic.
For such Bayesian networks deterministic part of the model can be represented
as a Boolean function, and a central part of belief updating reduces to the
task of calculating the number of satisfying configurations in a Boolean
function. In this paper we explore how advances in the calculation of Boolean
functions can be adopted for belief updating, in particular within the context
of troubleshooting. We present experimental results indicating a substantial
speed-up compared to traditional junction tree propagation.
