The mighty force: statistical inference and high-dimensional statistics
Disordered Systems and Neural Networks
2022-05-03 v1 Statistical Mechanics
Information Theory
math.IT
Abstract
This is a review to appear as a contribution to the edited volume "Spin Glass Theory & Far Beyond - Replica Symmetry Breaking after 40 Years", World Scientific. It showcases a selection of contributions from the spin glass community at large to high-dimensional statistics, by focusing on three important graph-based models and methodologies having deeply impacted the field: inference of graphs (a.k.a. direct coupling analysis), inference from graphs (the community detection problem), and the dynamic cavity method, which in particular allows for inference from graphs encoding causal relations.
Cite
@article{arxiv.2205.00750,
title = {The mighty force: statistical inference and high-dimensional statistics},
author = {Erik Aurell and Jean Barbier and Aurelien Decelle and Roberto Mulet},
journal= {arXiv preprint arXiv:2205.00750},
year = {2022}
}
Comments
To appear as a contribution to the edited volume "Spin Glass Theory & Far Beyond - Replica Symmetry Breaking after 40 Years", World Scientific