  In this paper we present a framework for dynamically constructing Bayesian
networks. We introduce the notion of a background knowledge base of schemata,
which is a collection of parameterized conditional probability statements.
These schemata explicitly separate the general knowledge of properties an
individual may have from the specific knowledge of particular individuals that
may have these properties. Knowledge of individuals can be combined with this
background knowledge to create Bayesian networks, which can then be used in any
propagation scheme. We discuss the theory and assumptions necessary for the
implementation of dynamic Bayesian networks, and indicate where our approach
may be useful.
