A Method for Implementing a Probabilistic Model as a Relational Database
arXiv:1302.4990
Abstract
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.
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)