An accessible account of the rich Theory surrounding Concentration inequalities in probability theory, with applications from machine learning and statistics to high-dimensional geometry.
This book introduces key ideas and presents a detailed summary of the state-of-the-art in the area, making it ideal for independent learning and as a reference..
An accessible account of the rich Theory surrounding Concentration inequalities in probability theory, with applications from machine learning and statistics to high-dimensional geometry