A dynamic approach to probabilistic
inference using Bayesian networks
In: Proceedings of the 6th Conference of Uncertainty in
, pp. 155-161, Cambridge, MA, 1990.
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
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.
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