Discrete Probability

Abstract

Includes index. Discrete Probability is a post-calculus-level textbook for a first course in probability. Because continuous probability is not treated, discrete probability can be covered in greater depth. The result is a book of special interest to students majoring in computer science as well as those majoring in mathematics. Because calculus is used only occasionally, students who have not had a recent course can nevertheless easily understand the book. The slow, gentle style and clear exposition will appeal to students. Basic concepts, such as counting, independence, conditional probability, random variables, approximation of probabilities, generating functions, random walks, and Markov chains, are presented with clear explanations and many worked-out exercises. An important feature of the book is the abundance of problems, which students may use to master the material. The 1,196 numerical answers to the 405 exercises, many with multiple parts, are included at the end of the book. Throughout the book appear various comments on the history of the study of probability. The author presents biographical information about some of the well-known contributors to probability, such as Fermat, Pascal, the Bernoullis, DeMoivre, Bayes, Laplace, Poisson, Markov, and many others. This volume will appeal to a wide range of readers, and should be useful in the undergraduate programs at many colleges and universities. Counting -- Independence and conditional probability -- Random variables -- More about random variables -- Approximating probabilities -- Generating functions -- Random walks -- Markov chains.