The seventh edition of the successful *Introduction to Probability Models* introduces elementary probability theory and the stochastic processes and is particularly well-suited to those applying probability theory to the study of phenomena in engineering, management science, the physical and social sciences, and operations research. Skillfully organized, *Introduction to Probability Models* covers all essential topics. Sheldon Ross, a talented and prolific textbook author, distinguishes this carefully and substantially revised book by his effort to develop in students an intuitive, and therefore lasting, grasp of probability theory. The seventh edition includes many new examples and exercises, with the majority of the new exercises being less demanding of the student. In addition, the text introduces stochastic processes, stressing applications, in an easily understood manner. There is a comprehensive introduction to the applied models of probability that stresses intuition. Both students and professors will agree that this is the most solid and widely used text for probability theory.

* Provides a detailed coverage of the Markov Chain Monte Carlo methods and Markov Chain covertimes

* Gives a thorough presentation of k-record values and the surprising Ignatov's theorem

* Includes examples relating to: "Random walks to circles," "The matching rounds problem," "The best prize problem" and many more

* Contains a comprehensive appendix with the answers to approximately 100 exercises from throughout the text

* Accompanied by a complete instructor's solutions manual with step-by-step solutions to all exercises

NEW TO THIS EDITION

* Includes many new and easier examples and exercises

* Offers new material on utilizing probabilistic method in combinatorial optimization problems

* Includes new material on suspended animation reliability models

* Contains new material on random algorithms and cycles of random permutations

*"synopsis" may belong to another edition of this title.*

The Seventh Edition of Ross' *Intorduction to Probability Models* represents the continuing convergence of this best-selling book with the widening indispensability of probability in pure and applied science.

Revised and updated, *Introduction to Probability Models* is particularly well suited to those seeking an understanding of how probability theory and stochastic processes apply to phenomena in such fields as engineering, management science, the physical and social sciences, and operations research.

While retaining its focus on elementary probability and stochastic processes, this edition's significant revisions include:

* Nearly 600 new or updated exercises, with over 100 solutions provided

* New derivations for the Poisson and nonhomogeneous Poisson processes

* Optimization of a single server, general service time queue

* Analysis of a series structure reliability model in which components enter a state of suspended animation upon cohort failure

**Sheldon M. Ross** has published numerous textbooks and technical articles in the areas of statistics and applied probability. Professor Ross is the founding and continuing editor of the journal *Probability in the Engineering and Informational Sciences,* published by Cambridge University Press. He is a fellow of the Institute of Mathematical Statistics and a recipient of the Humboldt U.S. Senior Scientist Award.

Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of Southern California. He received his Ph.D. in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal *Probability in the Engineering and Informational Sciences*. He is a Fellow of the Institute of Mathematical Statistics, and a recipient of the Humboldt US Senior Scientist Award.

*"About this title" may belong to another edition of this title.*

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