Compactly written, but nevertheless very readable, appealing to intuition, this introduction to probability theory is an excellent textbook for a one-semester course for undergraduates in any direction that uses probabilistic ideas. Technical machinery is only introduced when necessary. The route is rigorous but does not use measure theory. The text is illustrated with many original and surprising examples and problems taken from classical applications like gambling, geometry or graph theory, as well as from applications in biology, medicine, social sciences, sports, and coding theory. Only first-year calculus is required.

Recensione

"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

"It seems that a task to provide an introductory course on probablitity fulfilling the following requirements arises not so rarely: (A) The course should be accessible to studnets having only very modest preliminary knowledge of calculus, in particular, with no acquaintance with measure theory. (B) The presentation should be fully rigorous. (C) Nontrivial resuilts should be give. (D) Motivation for further strudy of measure theoretic probability ought to be provided, hence to contetn oneself to countable probability spaces is undesirable. R. Meester's book is an attametp to shot that all these demands may be fulfilled in a reasonalb eway, however incompatible they may look at first sight."

---Mathematica Bohemica

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