The Local Inconsistency Resolution Algorithm
Abstract
We present a generic algorithm for learning and approximate inference across a broad class of statistical models, that unifies many approaches in the literature. Our algorithm, called local inconsistency resolution (LIR), has an intuitive epistemic interpretation. It is based on the theory of probabilistic dependency graphs (PDGs), an expressive class of graphical models rooted in information theory, that can capture inconsistent beliefs.
Cite
Text
Richardson. "The Local Inconsistency Resolution Algorithm." ICML 2023 Workshops: SPIGM, 2023.Markdown
[Richardson. "The Local Inconsistency Resolution Algorithm." ICML 2023 Workshops: SPIGM, 2023.](https://mlanthology.org/icmlw/2023/richardson2023icmlw-local-a/)BibTeX
@inproceedings{richardson2023icmlw-local-a,
title = {{The Local Inconsistency Resolution Algorithm}},
author = {Richardson, Oliver Ethan},
booktitle = {ICML 2023 Workshops: SPIGM},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/richardson2023icmlw-local-a/}
}