Local Expression Languages for Probabilistic Dependence: a Preliminary Report
This work is incremental, offering a modest extension to existing probabilistic inference methods for specialized applications in belief networks.
The authors generalized the local expression language in Symbolic Probabilistic Inference (SPI) for belief nets by adding operators (*, +, -) to combine partial conditional distributions, enabling representation of relationships like 'noisy or' with semantic and inferential benefits.
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.