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Concentration Inequalities for Sums and Martingales
The purpose of this book is to provide an overview of historical and recent results on concentration inequalities for sums of independent random variables and for martingales. The first chapter is devoted to classical asymptotic results in probability such as the strong law of large numbers and the central limit theorem. Our goal is to show that it is really interesting to make use of concentration inequalities for sums and martingales. The second chapter deals with classical concentration inequalities for sums of independent random variables such as the famous Hoeffding, Bennett, Bernstein and Talagrand inequalities. Further results and improvements are also provided such as the missing factors in those inequalities. The third chapter concerns concentration inequalities for martingales such as Azuma-Hoeffding, Freedman and De la Pena inequalities. Several extensions are also provided. The fourth chapter is devoted to applications of concentration inequalities in probability and statistics.
Regents' Proceedings
None
Court of Appeals
None
New York Court of Appeals. Records and Briefs.
Volume contains: need index past index 6 (B. M. Heede, Inc. v. Roberts) need index past index 6 (B. M. Heede, Inc. v. Roberts) need index past index 6 (Matter of Herter) need index past index 6 (Matter of Herter) need index past index 6 (High v. Trade Union Courier Publishing Corp.) need index past index 6 (High v. Trade Union Courier Publishing Corp.) need index past index 6 (Matter of Hinzmann & Waldmann, Inc.) need index past index 6 (Matter of Hinzmann & Waldmann, Inc.) need index past index 6 (Matter of Horvat) need index past index 6 (People ex rel Hotel Paramount Corp. v. Chambers) need index past index 6 (People ex rel Hotel Paramount Corp. v. Chambers) need index past index 6 (Hyman...
Reports of the Tax Court of the United States
Final issue of each volume includes table of cases reported in the volume.
High Dimensional Probability VII
This volume collects selected papers from the 7th High Dimensional Probability meeting held at the Institut d'Études Scientifiques de Cargèse (IESC) in Corsica, France. High Dimensional Probability (HDP) is an area of mathematics that includes the study of probability distributions and limit theorems in infinite-dimensional spaces such as Hilbert spaces and Banach spaces. The most remarkable feature of this area is that it has resulted in the creation of powerful new tools and perspectives, whose range of application has led to interactions with other subfields of mathematics, statistics, and computer science. These include random matrices, nonparametric statistics, empirical processes, statistical learning theory, concentration of measure phenomena, strong and weak approximations, functional estimation, combinatorial optimization, and random graphs. The contributions in this volume show that HDP theory continues to thrive and develop new tools, methods, techniques and perspectives to analyze random phenomena.
Number Theory – Diophantine Problems, Uniform Distribution and Applications
This volume is dedicated to Robert F. Tichy on the occasion of his 60th birthday. Presenting 22 research and survey papers written by leading experts in their respective fields, it focuses on areas that align with Tichy’s research interests and which he significantly shaped, including Diophantine problems, asymptotic counting, uniform distribution and discrepancy of sequences (in theory and application), dynamical systems, prime numbers, and actuarial mathematics. Offering valuable insights into recent developments in these areas, the book will be of interest to researchers and graduate students engaged in number theory and its applications.
Judicial Code and Judiciary
Considers legislation to revise codification of Federal judicial organization, jurisdiction, venue, and court procedure.
