Representation of Bayesian Networks as Relational Databases

This paper suggests a representation of Bayesian networks based on a generalized relational database model. The main advantage of this representation is that it takes full advantage of the capabilities of conventional relational database systems for probabilistic inference. Belief update, for example, can be processed as an ordinary query, and the techniques for query optimization are directly applicable to updating beliefs. The results of this paper also establish a link between knowledge-based systems for probabilistic reasoning and relational databases.

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