Offering comprehensive coverage of modern probability theory (exclusive of continuous time stochastic processes), this unique book functions as both an introduction for graduate statisticians, mathematicians, engineers, and economists and an encyclopedic reference of the subject for professionals in these fields. It assumes only a knowledge of calculus as well as basic real analysis and linear algebra.

Throughout Theoretical Probability for Applications the focus is on the practical uses of this increasingly important tool. It develops topics of discrete time probability theory for use in a multitude of applications, including stochastic processes, theoretical statistics, and other disciplines that require a sound foundation in modern probability theory. Principles of measure theory related to the study of probability theory are developed as they are required throughout the book.

The book examines most of the basic probability models that involve only a finite or countably infinite number of random variables. Topics in the "Discrete Models" section include Bernoulli trials, random walks, matching, sums of indicators, multinomial trials. Poisson approximations and processes, sampling. Markov chains, and discrete renewal theory. Nondiscrete models discussed include univariate, Beta, sampling, and Dirichlet distributions as well as order statistics.

A separate chapter covers aspects of the multivariate normal model. Every treatment is carried out for both random vectors and random variables. Consequently, the book contains complete proofs of the vector case which often differ in detail from those of the scalar case

1. Complete with end-of-chapter exercises that provide both a drill of the material presented and an expansion of that same material, explanations of notations used, and a detailed bibliography. Theoretical Probability for Applications is a practical, easy-to-use reference which accommodates the diverse needs of statisticians, mathematicians, economists, engineers, instructors, and students alike.