Concentration Inequalities: A Nonasymptotic Theory of Independence
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The authors describe the interplay between the probabilistic structure independence and a variety of tools ranging from functional inequalities to transportation arguments to information theory. Applications to the study of empirical processes, random projections, random matrix theory, and threshold phenomena are also presented.
A self-contained introduction to concentration inequalities, it includes a survey of concentration of sums of independent random variables, variance bounds, the entropy method, and the transportation method. Deep connections with isoperimetric problems are revealed whilst special attention is paid to applications to the supremum of empirical processes. Written by leading experts in the field and containing extensive exercise sections this book will be an invaluable resource for researchers and graduate students in mathematics, theoretical computer science, and engineering.
The clear exposition from basic material up to recent sophisticated results and lucid writing style make the text a pleasure to read. Currently there are about students enrolled in various academic programs, about project employees in various sponsored projects, about 30 technical staff, 30 administrative staff and about 60 faculty members. The department, through the Electrical Engineering Students Association EESA, maintains regular contact with its alumni, many of whom are well-known and established in their careers across the world.
Timetable directs you the course timetable and exam timetable. Basics — Chernoff, Hoeffding, Efron-Stein inequalities 2.
Logarithmic Sobolev Inequality 5. Rumours and Epidemics — First passage percolation.
Concentration inequalities. A nonasymptotic theory of independence
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