Introduction to Probability Models.

TLDR: There is a comprehensive introduction to the applied models of probability that stresses intuition, and both professionals, researchers, and the interested reader will agree that this is the most solid and widely used book for probability theory.

Abstract: The Seventh Edition of the successful Introduction to Probability Models introduces elementary probability theory and stochastic processes. This book 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 book by his effort to develop in students an intuitive, and therefore lasting, grasp of probability theory. Ross' classic and best-selling text has been carefully and substantially revised. The Seventh Edition includes many new examples and exercises, with the majority of the new exercises being of the easier type. Also, the book 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 professionals, researchers, and the interested reader will agree that this is the most solid and widely used book for probability theory. Features: * 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