Uncertainty in Artificial Intelligence
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A Dynamic Approach to Probabilistic Inference
Michael Horsch, David Poole
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.
Pages: 155-161
PS Link:
PDF Link: /papers/90/p155-horsch.pdf
AUTHOR = "Michael Horsch and David Poole",
TITLE = "A Dynamic Approach to Probabilistic Inference",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "155--161"

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