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Limit Theorems of Probability Theory
A collection of research level surveys on certain topics in probability theory by a well-known group of researchers. The book will be of interest to graduate students and researchers.
A History of the Central Limit Theorem
This study discusses the history of the central limit theorem and related probabilistic limit theorems from about 1810 through 1950. In this context the book also describes the historical development of analytical probability theory and its tools, such as characteristic functions or moments. The central limit theorem was originally deduced by Laplace as a statement about approximations for the distributions of sums of independent random variables within the framework of classical probability, which focused upon specific problems and applications. Making this theorem an autonomous mathematical object was very important for the development of modern probability theory.
Limit Theorems For Associated Random Fields And Related Systems
This volume is devoted to the study of asymptotic properties of wide classes of stochastic systems arising in mathematical statistics, percolation theory, statistical physics and reliability theory. Attention is paid not only to positive and negative associations introduced in the pioneering papers by Harris, Lehmann, Esary, Proschan, Walkup, Fortuin, Kasteleyn and Ginibre, but also to new and more general dependence conditions. Naturally, this scope comprises families of independent real-valued random variables. A variety of important results and examples of Markov processes, random measures, stable distributions, Ising ferromagnets, interacting particle systems, stochastic differential equ...
Probability: The Classical Limit Theorems
A leading authority sheds light on a variety of interesting topics in which probability theory plays a key role.
Heads or Tails
Everyone knows some of the basics of probability, perhaps enough to play cards. Beyond the introductory ideas, there are many wonderful results that are unfamiliar to the layman, but which are well within our grasp to understand and appreciate. Some of the most remarkable results in probability are those that are related to limit theorems--statements about what happens when the trial is repeated many times. The most famous of these is the Law of Large Numbers, which mathematicians,engineers, economists, and many others use every day. In this book, Lesigne has made these limit theorems accessible by stating everything in terms of a game of tossing of a coin: heads or tails. In this way, the analysis becomes much clearer, helping establish the reader's intuition aboutprobability. Moreover, very little generality is lost, as many situations can be modelled from combinations of coin tosses. This book is suitable for anyone who would like to learn more about mathematical probability and has had a one-year undergraduate course in analysis.
Mathematical Statistics and Limit Theorems
This Festschrift in honour of Paul Deheuvels’ 65th birthday compiles recent research results in the area between mathematical statistics and probability theory with a special emphasis on limit theorems. The book brings together contributions from invited international experts to provide an up-to-date survey of the field. Written in textbook style, this collection of original material addresses researchers, PhD and advanced Master students with a solid grasp of mathematical statistics and probability theory.
Limit Theorems for Randomly Stopped Stochastic Processes
This volume is the first to present a state-of-the-art overview of this field, with many results published for the first time. It covers the general conditions as well as the basic applications of the theory, and it covers and demystifies the vast and technically demanding Russian literature in detail. Its coverage is thorough, streamlined and arranged according to difficulty.
Limit Theorems of Probability Theory
Limit laws for order statistics; Some notes on the law of the iterated logarithm for empirical distribution function; Some notes on the empirical distribution function and the quantile process; Law of large numbers for Markov chains homogeneous in time and in the second component; Learning from an ergodic training sequence; Around the Glivenko - Cantelli theorem; Gauss distributions and central limit theorem for locally compact groups; Limit problems on topological stochastic groups and bohr compactification; Weak convergence and embedding; The method of perturbation on the spectrum of linear operators in asymptotic problems of probability theory; Equivalence-orthogonality dichotomies of probability measures; On some asymptotical properties of recursive estimates; On the properties of the recursive estimates for a functional of an unknown distribution function; Some functional laws of the iterated logarithm for dependent random variables.
