TL;DR
This survey introduces Stein's method, explaining its core concepts and techniques for approximating various distributions, and explores its connections to concentration inequalities, aimed at graduate students in probability.
Contribution
It provides an accessible overview of Stein's method, highlighting common themes and foundational ideas across multiple distributional approximation techniques.
Findings
Clarifies the main concepts of Stein's method
Links Stein's method to concentration inequalities
Serves as an educational resource for beginners
Abstract
This survey article discusses the main concepts and techniques of Stein's method for distributional approximation by the normal, Poisson, exponential, and geometric distributions, and also its relation to concentration inequalities. The material is presented at a level accessible to beginning graduate students studying probability with the main emphasis on the themes that are common to these topics and also to much of the Stein's method literature.
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Taxonomy
TopicsRandom Matrices and Applications · Point processes and geometric inequalities · Stochastic processes and statistical mechanics