A language for construction of belief networks

A method for incrementally constructing belief networks, which are directed acyclic graph representations for probability distributions, is described. A network-construction language, FRAIL3, which is similar to a forward-chaining language using data dependencies but has additional features for specifying distributions, was developed. A particularly important feature of this language is that is allows the user to conveniently specify conditional probability matrices using stereotyped models of intercausal interaction. Using FRAIL3, one can define parmeterized 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.<
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