Computing Probability Intervals Under Independency Constraints
Abstract
Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a fully specified joint probability distribution is available, and conclude that it is not suitable for application in AI systems. Probability intervals, however, constitute a means for expressing incompleteness of information. We present a method for computing probability interval! for probabilities of interest from a partial specification of a joint probability distribution. Our method improves on earlier approaches by all owing for independency relation ships between statistical variables to be exploited .
Cite
Text
van der Gaag. "Computing Probability Intervals Under Independency Constraints." Conference on Uncertainty in Artificial Intelligence, 1990.Markdown
[van der Gaag. "Computing Probability Intervals Under Independency Constraints." Conference on Uncertainty in Artificial Intelligence, 1990.](https://mlanthology.org/uai/1990/vandergaag1990uai-computing/)BibTeX
@inproceedings{vandergaag1990uai-computing,
title = {{Computing Probability Intervals Under Independency Constraints}},
author = {van der Gaag, Linda C.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1990},
pages = {457-466},
url = {https://mlanthology.org/uai/1990/vandergaag1990uai-computing/}
}