  We describe a method for incrementally constructing belief networks. We have
developed a network-construction language similar to a forward-chaining
language using data dependencies, but with additional features for specifying
distributions. Using this language, we can define parameterized classes of
probabilistic models. These parameterized models make it possible to apply
probabilistic reasoning to problems for which it is impractical to have a
single large static model.
