A covariance representation and an elementary proof of the Gaussian concentration inequality
Christian Houdr\'e
TL;DR
This paper introduces a covariance-based approach using characteristic functions to provide an elementary proof of the Gaussian concentration inequality, simplifying understanding and potentially broadening its applications.
Contribution
It offers a new, elementary proof of the Gaussian concentration inequality through a covariance representation, enhancing accessibility and understanding.
Findings
Elementary proof of Gaussian concentration inequality
Covariance representation based on characteristic functions
Brief mention of additional applications
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.
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Taxonomy
TopicsStatistical Methods and Bayesian Inference · Gaussian Processes and Bayesian Inference · Bayesian Methods and Mixture Models