Random Matrices, Intrinsic Freeness, and Sharp Non-Asymptotic Inequalities

Fuente: arXiv
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Main Author: Bandeira, Afonso S.
Format: Preprint
Published: 2025
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author Bandeira, Afonso S.
author_facet Bandeira, Afonso S.
contents Random matrix theory has played a major role in several areas of pure and applied mathematics, as well as statistics, physics, and computer science. This lecture aims to describe the intrinsic freeness phenomenon and how it provides new easy-to-use sharp non-asymptotic bounds on the spectrum of general random matrices. We will also present a couple of illustrative applications in high dimensional statistical inference. This article accompanies a lecture that will be given by the author at the International Congress of Mathematicians in Philadelphia in the Summer of 2026.
format Preprint
id arxiv_https___arxiv_org_abs_2510_01021
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Random Matrices, Intrinsic Freeness, and Sharp Non-Asymptotic Inequalities
Bandeira, Afonso S.
Probability
Data Structures and Algorithms
Statistics Theory
Random matrix theory has played a major role in several areas of pure and applied mathematics, as well as statistics, physics, and computer science. This lecture aims to describe the intrinsic freeness phenomenon and how it provides new easy-to-use sharp non-asymptotic bounds on the spectrum of general random matrices. We will also present a couple of illustrative applications in high dimensional statistical inference. This article accompanies a lecture that will be given by the author at the International Congress of Mathematicians in Philadelphia in the Summer of 2026.
title Random Matrices, Intrinsic Freeness, and Sharp Non-Asymptotic Inequalities
topic Probability
Data Structures and Algorithms
Statistics Theory
url https://arxiv.org/abs/2510.01021