Some compact notations for concentration inequalities and user-friendly results
Statistics Theory
2020-04-28 v2 Information Theory
Machine Learning
math.IT
Probability
Machine Learning
Statistics Theory
Abstract
This paper presents compact notations for concentration inequalities and convenient results to streamline probabilistic analysis. The new expressions describe the typical sizes and tails of random variables, allowing for simple operations without heavy use of inessential constants. They bridge classical asymptotic notations and modern non-asymptotic tail bounds together. Examples of different kinds demonstrate their efficacy.
Cite
@article{arxiv.1912.13463,
title = {Some compact notations for concentration inequalities and user-friendly results},
author = {Kaizheng Wang},
journal= {arXiv preprint arXiv:1912.13463},
year = {2020}
}
Comments
10 pages