Includes bibliographical references (pages 191-192) and index.
Experiments -- Random Variables and Random Vectors -- Random Walk -- Limit Theorems -- Intermezzo -- Continuous Random Variables and Vectors -- Infinitely Many Repetitions -- The Poisson Process -- Limit Theorems -- Extending the Probabilities.
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"The book [is] an excellent new introductory text on probability. The classical way of teaching probability is based on measure theory. In this book discrete and continuous probability are studied with mathematical precision, within the realm of Riemann integration and not using notions from measure theory ... Numerous topics are discussed, such as: random walks, weak laws of large numbers, infinitely many repetitions, strong laws of large numbers, branching processes, weak convergence and [the] central limit theorem. The theory is illustrated with many original and surprising examples and problems." Zentralblatt Math "Most textbooks designed for a one-year course in mathematical statistics cover probability in the first few chapters as preparation for the statistics to come. This book in some ways resembles the first part of such textbooks: it's all probability, no statistics. But it does the probability more fully than usual, spending lots of time on motivation, explanation, and rigorous development of the mathematics ... The exposition is usually clear and eloquent ... Overall, this is a five-star book on probability that could be used as a textbook or as a supplement." MAA online.