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Journal of Intelligent Information Systems

, Volume 9, Issue 2, pp 181–202 | Cite as

An Extended Relational Data Model For Probabilistic Reasoning

  • S.K.M. Wong
Article

Abstract

Probabilistic methods provide a formalism for reasoning aboutpartial beliefs under conditions of uncertainty. This paper suggests a newrepresentation of probabilistic knowledge. This representation encompassesthe traditional relational database model. In particular, it is shown thatprobabilistic conditional independence is equivalent to the notion of generalized multivalued dependency. More importantly,a Markov network can be viewed as a generalized acyclic joindependency. This linkage between these two apparently different butclosely related knowledge representations provides a foundation fordeveloping a unified model for probabilistic reasoning and relationaldatabase systems.

Relational database probabilistic reasoning knowledge representation generalized acyclic join dependency belief networks 

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Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • S.K.M. Wong
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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