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.

Authors

• S. K. Michael Wong
• Cory J. Butz
• Yang Xiang

Keywords

• Probabilistic inference
• belief networks
• extended relational data model
• evidential