Introduction to Probability Models, Twelfth Edition, is the latest version of Sheldon Ross's classic bestseller. This trusted book introduces the reader to elementary probability modelling and stochastic processes and shows how probability theory can be applied in fields such as engineering, computer science, management science, the physical and social sciences and operations research. The hallmark features of this text have been retained in this edition, including a superior writing style and excellent exercises and examples covering the wide breadth of coverage of probability topics. In addition, many real-world applications in engineering, science, business and economics are included.

• Winner of a 2020 Textbook Excellence Award (College) (Texty) from the Textbook and Academic Authors Association
• Retains the valuable organization and trusted coverage that students and professors have relied on since 1972
• Includes new coverage on coupling methods, renewal theory, queueing theory, and a new derivation of Poisson process
• Offers updated examples and exercises throughout, along with required material for Exam 3 of the Society of Actuaries

1. Introduction to Probability Theory
2. Random Variables
3. Conditional Probability and Conditional Expectation
4. Markov Chains
5. The Exponential Distribution and the Poisson Process
6. Continuous-Time Markov Chains
7. Renewal Theory and Its Applications
8. Queueing Theory
9. Reliability Theory
10. Brownian Motion and Stationary Processes
11. Simulation
12. Coupling"The appearance in the Academic Press imprint of the twelfth edition of this book first published in 1972 gives an opportunity for a very belated notice in these pages. The first third of the book covers the elementary theory of probability, random variables, conditional probability and Markov chains. Chapter 5 focusses on the exponential distribution and Poisson processes, and then there are chapters on continuous-time Markov chains, renewal theory, queueing theory, reliability theory, Brownian motion and stationary processes, and simulation. This twelfth edition adds a short chapter on coupling and stochastic order. The treatment throughout is straightforward and fairly informal, consisting almost entirely of theory followed by examples, the latter being drawn from many different fields. Convergence theorems and the like are ignored. Nor is there any mention, for example, of sigma algebras; on page 3 we are simply given, for mutually exclusive events. Nor, at the other extreme, is there any discussion of the modelling assumptions or consideration of whether any given model is valid. At the end of each chapter is a lengthy exercise, usually of well over thirty questions and sometimes over eighty; a small number (starred) have solutions in the back, while there is a separate free instructors’ manual giving all solutions. The approach is very much that of Feller [1], while combining discrete and continuous distributions into one volume. In a book of this length and scope the index is of crucial importance; here it is mainly (but not entirely) concerned with the mathematics rather than the applications. I’m not sure how helpful this would be for users looking for information on specific applications used in the examples. That the book has continued to be reissued in new editions over a period of 47 years speaks for itself. It is obviously a most useful and wide-ranging reference resource, and it could also be used as the basis for numerous different university courses (including actuarial science), although its length, cost and bulk (over 1.5 kg) are somewhat intimidating. Apart from a few unfortunate page-breaks, the layout and other production values are excellent." --The Mathematical Gazette