An Extended Relational Data Model For Probabilistic Reasoning

摘要

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.