Concentration Inequalities: A Nonasymptotic Theory of Independence
by Gabor Lugosi, Stéphane Boucheron, Pascal Massart
Concentration Inequalities: A Nonasymptotic Theory of Independence by Gabor Lugosi, Stéphane Boucheron, and Pascal Massart explores the fundamentals of concentration bounds for independent random variables. This work provides a rigorous, nonasymptotic perspective essential for probability theory, statistical analysis, and related fields, offering precise tools for modern mathematical research.
About This Book
This book delves into concentration inequalities, focusing on a nonasymptotic approach to independence in probability theory. It offers a comprehensive framework for understanding how random variables behave under independence assumptions.
The authors present key results and techniques that are vital for applications in statistical learning and high-dimensional data analysis. The content emphasizes precise bounds without relying on asymptotic approximations.
Designed for researchers and advanced students, the text builds a solid theoretical foundation while highlighting practical implications in various mathematical contexts.
Through detailed proofs and examples, it bridges classical probability with contemporary challenges in independence and concentration phenomena.
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