  We present a generalization of the local expression language used in the
Symbolic Probabilistic Inference (SPI) approach to inference in belief nets
[1l, [8]. The local expression language in SPI is the language in which the
dependence of a node on its antecedents is described. The original language
represented the dependence as a single monolithic conditional probability
distribution. The extended language provides a set of operators (*, +, and -)
which can be used to specify methods for combining partial conditional
distributions. As one instance of the utility of this extension, we show how
this extended language can be used to capture the semantics, representational
advantages, and inferential complexity advantages of the "noisy or"
relationship.
