A Method for Implementing a Probabilistic Model as a Relational Database
  This paper discusses a method for implementing a probabilistic inference
system based on an extended relational data model. This model provides a
unified approach for a variety of applications such as dynamic programming,
solving sparse linear equations, and constraint propagation. In this framework,
the probability model is represented as a generalized relational database.
Subsequent probabilistic requests can be processed as standard relational
queries. Conventional database management systems can be easily adopted for
implementing such an approximate reasoning system.
