Symbolic Probabilitistic Inference in Large BN2O Networks
arXiv:1302.6795
Abstract
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.
Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)