Concentration of measure, classification of submeasures, and dynamics of $L_{0}$
Probability
2020-12-23 v3 Dynamical Systems
Logic
Abstract
Exhibiting a new type of measure concentration, we prove uniform concentration bounds for measurable Lipschitz functions on product spaces, where Lipschitz is taken with respect to the metric induced by a weighted covering of the index set of the product. Our proof combines the Herbst argument with an analogue of Shearer's lemma for differential entropy. We give a quantitative "geometric" classification of diffuse submeasures into elliptic, parabolic, and hyperbolic. We prove that any non-elliptic submeasure (for example, any measure, or any pathological submeasure) has a property that we call covering concentration. Our results have strong consequences for the dynamics of the corresponding topological -groups.
Keywords
Cite
@article{arxiv.1907.12686,
title = {Concentration of measure, classification of submeasures, and dynamics of $L_{0}$},
author = {Friedrich Martin Schneider and Sławomir Solecki},
journal= {arXiv preprint arXiv:1907.12686},
year = {2020}
}