  A BN2O network is a two level belief net in which the parent interactions are
modeled using the noisy-or interaction model. In this paper we discuss
application of the SPI local expression language to efficient inference in
large BN2O networks. In particular, we show that there is significant
structure, which can be exploited to improve over the Quickscore result. We
further describe how symbolic techniques can provide information which can
significantly reduce the computation required for computing all cause posterior
marginals. Finally, we present a novel approximation technique with preliminary
experimental results.
