Dynamic Construction of Belief Networks

TL;DR

This paper introduces a method for incrementally building belief networks using a specialized language, enabling flexible probabilistic reasoning for complex problems that are difficult to model with static networks.

Contribution

It presents a novel network-construction language and a framework for creating parameterized probabilistic models that support dynamic belief network construction.

Findings

Enables incremental belief network construction

Supports defining parameterized probabilistic models

Facilitates reasoning in complex, large-scale problems

Abstract

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.