Relevance for Robust Bayesian Network MAP-Explanations
Abstract
In the context of explainable AI, the concept of MAP-independence was recently introduced as a means for conveying the (ir)relevance of intermediate nodes for MAP computations in Bayesian networks. In this paper, we further study the concept of MAP-independence, discuss methods for finding sets of relevant nodes, and suggest ways to use these in providing users with an explanation concerning the robustness of the MAP result.
Cite
Text
Renooij. "Relevance for Robust Bayesian Network MAP-Explanations." Proceedings of The 11th International Conference on Probabilistic Graphical Models, 2022.Markdown
[Renooij. "Relevance for Robust Bayesian Network MAP-Explanations." Proceedings of The 11th International Conference on Probabilistic Graphical Models, 2022.](https://mlanthology.org/pgm/2022/renooij2022pgm-relevance/)BibTeX
@inproceedings{renooij2022pgm-relevance,
title = {{Relevance for Robust Bayesian Network MAP-Explanations}},
author = {Renooij, Silja},
booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models},
year = {2022},
pages = {13-24},
volume = {186},
url = {https://mlanthology.org/pgm/2022/renooij2022pgm-relevance/}
}