About the First Edition: The study of any topic becomes more meaningful if one also studies the historical development that resulted in the final theorem. ... This is an excellent book on mathematics in the making. --Philip Peak, The Mathematics Teacher, May, 1975 I find the book very interesting. It contains valuable information and useful references. It can be recommended not only to historians of science and mathematics but also to students of probability and statistics. --Wei-Ching Chang, Historica Mathematica, August, 1976 In the months since I wrote ... I have read it from cover to cover at least once and perused it here and there a number of times. I still find it a very interesting and worthwhile contribution to the history of probability and statistics. --Churchill Eisenhart, past president of the American Statistical Association, in a letter to the author, February 3, 1975 The name Central Limit Theorem covers a wide variety of results involving the determination of necessary and sufficient conditions under which sums of independent random variables, suitably standardized, have cumulative distribution functions close to the Gaussian distribution. As the name Central Limit Theorem suggests, it is a centerpiece of probability theory which also carries over to statistics. Part One of The Life and Times of the Central Limit Theorem, Second Edition traces its fascinating history from seeds sown by Jacob Bernoulli to use of integrals of $\exp (x^2)$ as an approximation tool, the development of the theory of errors of observation, problems in mathematical astronomy, the emergence of the hypothesis of elementary errors, the fundamental work of Laplace, and the emergence of an abstract Central Limit Theorem through the work of Chebyshev, Markov and Lyapunov. This closes the classical period of the life of the Central Limit Theorem, 1713-1901. The second part of the book includes papers by Feller and Le Cam, as well as comments by Doob, Trotter, and Pollard, describing the modern history of the Central Limit Theorem (1920-1937), in particular through contributions of Lindeberg, Cramer, Levy, and Feller. The Appendix to the book contains four fundamental papers by Lyapunov on the Central Limit Theorem, made available in English for the first time.

This elementary introduction to probability theory and information theory is suitable as a textbook for beginning students in mathematics, statistics or computer science who have some knowledge of basic calculus. It provides a clear and systematic foundation to the subject; the concept of probability is given particular attention via a highly simplified discussion of measures on Boolean algebras. The theoretical ideas are then applied to practical areas such as statistical inference, random walks, statistical mechanics and communications modelling. Topics dealt with include discrete and continuous random variables, entropy and mutual information, maximum entropy methods, the central limit theorem and the coding and transmission of information. Many examples and exercises are included that illustrate how the theory can be applied, for example to information technology. Detailed solutions to most exercises are available electronically from the Cambridge WWW server.

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.

An esteemed professor and one-time chairman of the mathematics department at New York's Pace University, Adams, interested in all facets of university administration, has produced an almost Jeffersonian volume of correspondence from his tenure. His views on textbook selection, collective bargaining and the proper role of the university have all flowed from his notebook, and no problem was too minute to evade his scope The frivolity of some of these papers is balanced by Adams's opinions on weightier issues, including sexual harassment and compensation in higher education. His approach and forward manner on these situations, despite how genuine, sometimes engendered resentment from his fellow faculty. But for those interested in the particulars of an academic career, this book offers a glimpse of what life may really be like inside the ivory tower. - Kirkus Discoveries-

This book is written for MBA students and statistics teachers of Anna University affiliated institutions. It is a natural outcome of the author, after teaching statistics several times for MBA students. It is shaped and reshaped several times to heal the heart and soul of non-mathematical community. This book covers all models in all chapters, completely. Problem identification is the most difficult task in statistics for the students. Hence a new approach with short cuts is used throughout the book to simplify this task. This book differs from other books in approaching the sampling distribution. Sampling distribution chapter is usually classified based on standard error, confidence interval estimation and hypothesis testing; or based on the number of sample sets – one sample, two sample; or based on the sample size – large sample, small sample; or based on the statistical tables – such as z test, t test, chi square test and F test. But this book classifies the sampling distribution chapter based on the parameters – Mean, Proportion, Standard deviation cum Variance. Because the very first thing to be decided by any researcher or a practitioner or a decision maker before applying any statistical tool is the parameter to be used and none other! The syllabus is reorganized as per the convenience of the students. 2nd and 3rd unit of the syllabus is reorganized based on the parameters – Mean, Proportion and Standard deviation instead of standard error, interval estimation and hypothesis testing. Similarly the discrete distributions – binomial & Poisson distributions and Probability rules of 1st unit are dealt at the end. Probability rules are grouped based on two models – associated events and successive events. Each Chapter contains illustrations, examples, formula, short answers and exercise.

This book contains an in-depth treatment of matrix-exponential (ME) distributions and their sub-class of phase-type (PH) distributions. Loosely speaking, an ME distribution is obtained through replacing the intensity parameter in an exponential distribution by a matrix. The ME distributions can also be identified as the class of non-negative distributions with rational Laplace transforms. If the matrix has the structure of a sub-intensity matrix for a Markov jump process we obtain a PH distribution which allows for nice probabilistic interpretations facilitating the derivation of exact solutions and closed form formulas. The full potential of ME and PH unfolds in their use in stochastic modelling. Several chapters on generic applications, like renewal theory, random walks and regenerative processes, are included together with some specific examples from queueing theory and insurance risk. We emphasize our intention towards applications by including an extensive treatment on statistical methods for PH distributions and related processes that will allow practitioners to calibrate models to real data. Aimed as a textbook for graduate students in applied probability and statistics, the book provides all the necessary background on Poisson processes, Markov chains, jump processes, martingales and re-generative methods. It is our hope that the provided background may encourage researchers and practitioners from other fields, like biology, genetics and medicine, who wish to become acquainted with the matrix-exponential method and its applications.

This book develops appreciation of the ingenuity involved in the mathematical treatment of random phenomena, and of the power of the mathematical methods employed in the solution of applied problems. It is intended to students interested in applications of probability to their disciplines.

This text is designed for an introductory probability course at the university level for undergraduates in mathematics, the physical and social sciences, engineering, and computer science. It presents a thorough treatment of probability ideas and techniques necessary for a firm understanding of the subject.

This incorporation of computer use into teaching and learning stochastic processes takes an applications- and computer-oriented approach rather than a mathematically rigorous approach. Solutions Manual available to instructors upon request. 1997 edition.