Knowledge Integration for Conditional Probability Assessments
Artificial Intelligence
2013-03-25 v1
Abstract
In the probabilistic approach to uncertainty management the input knowledge is usually represented by means of some probability distributions. In this paper we assume that the input knowledge is given by two discrete conditional probability distributions, represented by two stochastic matrices P and Q. The consistency of the knowledge base is analyzed. Coherence conditions and explicit formulas for the extension to marginal distributions are obtained in some special cases.
Cite
@article{arxiv.1303.5404,
title = {Knowledge Integration for Conditional Probability Assessments},
author = {Angelo Gilio and Fulvio Spezzaferri},
journal= {arXiv preprint arXiv:1303.5404},
year = {2013}
}
Comments
Appears in Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI1992)