A Method for Implementing a Probabilistic Model as a Relational Database
Artificial Intelligence
2013-02-21 v1
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.
Cite
@article{arxiv.1302.4990,
title = {A Method for Implementing a Probabilistic Model as a Relational Database},
author = {Michael S. K. M. Wong and C. J. Butz and Yang Xiang},
journal= {arXiv preprint arXiv:1302.4990},
year = {2013}
}
Comments
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)