A Method for Implementing a Probabilistic Model as a Relational Database
Michael Wong, C. Butz, Yang Xiang
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.
Keywords: Probabilistic inference, belief networks,
extended relational data model, evidential
PS Link: http://www.cs.uregina.ca/~butz/publications/implement/implement.html
PDF Link: /papers/95/p556-wong.pdf
AUTHOR = "Michael Wong
and C. Butz and Yang Xiang",
TITLE = "A Method for Implementing a Probabilistic Model as a Relational Database",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "556--564"