An Extended Relational Data Model For Probabilistic Reasoning

Abstract

Probabilistic methods provide a formalism for reasoning about partial beliefs under conditions of uncertainty. This paper suggests a new representation of probabilistic knowledge. This representation encompasses the traditional relational database model. In particular, it is shown that probabilistic conditional independence is equivalent to the notion of generalized multivalued dependency. More importantly, a Markov network can be viewed as a generalized acyclic join dependency. This linkage between these two apparently different but closely related knowledge representations provides a foundation for developing a unified model for probabilistic reasoning and relational database systems.