A covariance representation and an elementary proof of the Gaussian concentration inequality
Probability
2024-10-10 v1 Machine Learning
Abstract
Via a covariance representation based on characteristic functions, a known elementary proof of the Gaussian concentration inequality is presented. A few other applications are briefly mentioned.
Cite
@article{arxiv.2410.06937,
title = {A covariance representation and an elementary proof of the Gaussian concentration inequality},
author = {Christian Houdré},
journal= {arXiv preprint arXiv:2410.06937},
year = {2024}
}
Comments
These simple notes on well known results will not be published elsewhere. They have been previously referenced and are posted here to make them more widely accessible