An irreverent look at how randomness influences our lives, and how our successes and failures are far more dependent on chance events than we recognize.

· Introduction: The Nature of Probability Theory· The Sample Space· Elements of Combinatorial Analysis· Fluctuations in Coin Tossing and Random Walks· Combination of Events· Conditional Probability· Stochastic Independence· The Binomial and Poisson Distributions· The Normal Approximation to the Binomial Distribution· Unlimited Sequences of Bernoulli Trials· Random Variables· Expectation· Laws of Large Numbers· Integral Valued Variables· Generating Functions· Compound Distributions· Branching Processes· Recurrent Events· Renewal Theory· Random Walk and Ruin Problems· Markov Chains· Algebraic Treatment of Finite Markov Chains· The Simplest Time-Dependent Stochastic Processes

Sinai's book leads the student through the standard material for Probability Theory, 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, based on 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 used by more advanced readers who want to learn the mathematics of probability theory and some of its applications in statistical physics.

First published in 1990, this is a reissue of Professor Hilary Putname(tm)s dissertation thesis, written in 1951, which concerns itself with The Meaning of the Concept of Probability in Application to Finite Sequences and the problems of the deductive justification for induction. Written under the direction of Putname(tm)s mentor, Hans Reichenbach, the book considers Reichenbache(tm)s idealization of very long finite sequences as infinite sequences and the bearing this has upon Reichenbache(tm)s pragmatic vindication of induction.