A Graph-Theoretic Analysis of Information Value
Artificial Intelligence
2013-02-18 v1
Abstract
We derive qualitative relationships about the informational relevance of variables in graphical decision models based on a consideration of the topology of the models. Specifically, we identify dominance relations for the expected value of information on chance variables in terms of their position and relationships in influence diagrams. The qualitative relationships can be harnessed to generate nonnumerical procedures for ordering uncertain variables in a decision model by their informational relevance.
Cite
@article{arxiv.1302.3596,
title = {A Graph-Theoretic Analysis of Information Value},
author = {Kim-Leng Poh and Eric J. Horvitz},
journal= {arXiv preprint arXiv:1302.3596},
year = {2013}
}
Comments
Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996)