Inequality has become an essential tool in many areas of mathematical research, for example in probability and statistics where it is frequently used in the proofs. "Probability Inequalities" covers inequalities related with events, distribution functions, characteristic functions, moments and random variables (elements) and their sum. The book shall serve as a useful tool and reference for scientists in the areas of probability and statistics, and applied mathematics. Prof. Zhengyan Lin is a fellow of the Institute of Mathematical Statistics and currently a professor at Zhejiang University, Hangzhou, China. He is the prize winner of National Natural Science Award of China in 1997. Prof. Zhidong Bai is a fellow of TWAS and the Institute of Mathematical Statistics; he is a professor at the National University of Singapore and Northeast Normal University, Changchun, China.
Setting / LocationGlobal
Story EraModern Era
Protagonist / CoreProbability Inequalities Lead Character
This is the eighth volume (out of a projected ten) with papers which appeared during the "Stochastic Equations and Their Applications" year (1985-1986) at the Institute for Mathematics and its Applications at the University of Minnesota. This volume, which is directed towards researchers in applied mathematics, engineering, and physics, contains contributions by: M. Aizeman, R. Holley, M. Bramson, D. Griffeath, D.C. Brydges, J.T. Chayes, L. Chayes, J.T. Cox, R. Durrett, R.H. Schonmann, S. Goldstein, L. Gray, J.W. Halley, R. Holley, H. Kesten, T.M. Leggett, C.M. Newman, H.E. Stanley, D. Stauffer, and J.C. Wierman.
Sinai's book leads the student through the standard material for ProbabilityTheory, with stops along the way for interesting topics such as statistical mechanics, not usually included in a book for beginners. The first part of the book covers discrete random variables, using the same approach, basedon Kolmogorov's axioms for probability, used later for the general case. The text is divided into sixteen lectures, each covering a major topic. The introductory notions and classical results are included, of course: random variables, the central limit theorem, the law of large numbers, conditional probability, random walks, etc. Sinai's style is accessible and clear, with interesting examples to accompany new ideas. Besides statistical mechanics, other interesting, less common topics found in the book are: percolation, the concept of stability in the central limit theorem and the study of probability of large deviations. Little more than a standard undergraduate course in analysis is assumed of the reader. Notions from measure theory and Lebesgue integration are introduced in the second half of the text. The book is suitable for second or third year students in mathematics, physics or other natural sciences. It could also be usedby more advanced readers who want to learn the mathematics of probability theory and some of its applications in statistical physics.
This book contains the proceedings of the International Conference on Mathematical Results in Quantum Mechanics held in Blossin, Germany, May 17-21, 1993. Its purpose is to draw attention to the recent developments in quantum mechanics and related mathematical problems. The book is addressed to the wide audience of mathematicians and physicists interested in contemporary quantum physics and associated mathematical problems. The reader will find sections not only on traditional subjects such as Schrödinger and Dirac operators and generalized Schrödinger generators, but also on stochastic spectral analysis, many-body problems and statistical physics, chaos, and operator theory and its applications. Contributors: Schrödinger and Dirac operators: M.Sh. Birman, V. Grecchi, R. Hempel, M. Hoffmann-Ostenhof, Y. Saito, G. Stolz, M. Znojil • Generalized Schrödinger operators: J.-P. Antoine, J.F. Brasche, P. Duclos, R. Hempel, M. Klein, P. Stovicek • Stochastic spectral analysis: M. Demuth, V.A. Liskevich, E.M. Ouhabaz, P. Stollmann • Many-body problems and statistical physics: M. Fannes, R. Gielerak, M. Hübner, A.M. Khorunzhy, H. Lange, N. Macris, Yu.A. Petrina, K.B. Sinha, A. Verbeure • Chaos: J. Dittrich, P. Seba, K. Zyczkowski • Operator theory and its application: F. Bentosela, V. Buslaev, A.N. Kochubei, A.Yu. Konstantinov, V. Koshmanenko, H. Neidhardt, G. Nenciu, D. Robert
Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input? How much training is required to achieve a specified level of accuracy in the prediction? How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite interval of time? In its successful first edition, A Theory of Learning and Generalization was the first book to treat the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side-by-side leads to new insights, as well as to new results in both topics. This second edition extends and improves upon this material, covering new areas including: Support vector machines. Fat-shattering dimensions and applications to neural network learning. Learning with dependent samples generated by a beta-mixing process. Connections between system identification and learning theory. Probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithm. Reflecting advancements in the field, solutions to some of the open problems posed in the first edition are presented, while new open problems have been added. Learning and Generalization (second edition) is essential reading for control and system theorists, neural network researchers, theoretical computer scientists and probabilist.
Probability Inequalities by Zhengyan Lin, Zhidong Bai | Literally Just Books
