Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models
Abstract
The semigraphoid closure of every couple of CI-statements (GI=conditional independence) is a stochastic CI-model. As a consequence of this result it is shown that every probabilistically sound inference rule for CI-model, having at most two antecedents, is derivable from the semigraphoid inference rules. This justifies the use of semigraphoids as approximations of stochastic CI-models in probabilistic reasoning. The list of all 19 potential dominant elements of the mentioned semigraphoid closure is given as a byproduct.
Cite
Text
Studený. "Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models." Conference on Uncertainty in Artificial Intelligence, 1994. doi:10.1016/B978-1-55860-332-5.50074-2Markdown
[Studený. "Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models." Conference on Uncertainty in Artificial Intelligence, 1994.](https://mlanthology.org/uai/1994/studeny1994uai-semigraphoids/) doi:10.1016/B978-1-55860-332-5.50074-2BibTeX
@inproceedings{studeny1994uai-semigraphoids,
title = {{Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models}},
author = {Studený, Milan},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1994},
pages = {546-552},
doi = {10.1016/B978-1-55860-332-5.50074-2},
url = {https://mlanthology.org/uai/1994/studeny1994uai-semigraphoids/}
}