Dynamic Construction of Belief Networks
This work addresses the challenge of applying probabilistic reasoning to complex problems by allowing dynamic model construction, though it appears incremental as it builds on existing forward-chaining and dependency concepts.
The authors tackled the problem of constructing belief networks incrementally by developing a network-construction language with features for specifying distributions, enabling the definition of parameterized probabilistic models for problems where large static models are impractical.
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.