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【书名】 Introduction to Probability Models
【作者】 Sheldon M. Ross
【出版社】Academic Press
【版本】第九版
【出版日期】2006-11-21
【文件格式】清晰PDF
【文件大小】3.65m
【页数】801
【ISBN出版号】978-0-12-598062-3
【资料类别】计量经济学，统计学
【市面定价】USD 99.95
【扫描版还是影印版】清晰
【是否缺页】没有
【关键词】Probability Models
【内容简介】
x Provides a detailed coverage of the Markov Chain Monte Carlo methods and Markov Chain covertimes
x Gives a thorough presentation of k-record values and the surprising Ignatov's theorem
x Includes examples relating to: "Random walks to circles," "The matching rounds problem," "The best prize problem" and many more
x Contains a comprehensive appendix with the answers to approximately 100 exercises from throughout the text
x Accompanied by a complete instructor's solutions manual with step-by-step solutions to all exercises
NEW TO THIS EDITION
x Includes many new and easier examples and exercises
x Offers new material on utilizing probabilistic method in combinatorial optimization problems
x Includes new material on suspended animation reliability models
x Contains new material on random algorithms and cycles of random permutations
About the Author
Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. He received his Ph.D. in statistics at Stanford University in 1968 and has been at Berkeley ever since. 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, Fourth Edition published by MacMillan, Introduction to Probability Models, Fifth Edition published by Academic Press, Stochastic Processes, Second Edition published by Wiley, and a new text, Introductory Statistics published by McGraw Hill. 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 US Senior Scientist Award.
University of California, Berkeley, U.S.A.
【目录】
Introduction to Probability Theory
Random Variables.
Conditional Probability and Conditional. Expectation.
Markov Chains.
The Exponential Distribution and the Poisson Process.
Continuous-Time Markov Chains.
Renewal Theory and Its Applications.
Queuing Theory.
Reliability Theory.
Brownian Motion and Stationary Processes.
Simulation.
【整理书评】
Ross's classic bestseller, Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It provides an introduction to elementary probability theory and stochastic processes, and shows how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries.
A new section (3.7) on COMPOUND RANDOM VARIABLES, that can be used to establish a recursive formula for computing probability mass functions for a variety of common compounding distributions.
A new section (4.11) on HIDDDEN MARKOV CHAINS, including the forward and backward approaches for computing the joint probability mass function of the signals, as well as the Viterbi algorithm for determining the most likely sequence of states.
Simplified Approach for Analyzing Nonhomogeneous Poisson processes
Additional results on queues relating to the
(a) conditional distribution of the number found by an M/M/1 arrival who spends a time t in the system,;
(b) inspection paradox for M/M/1 queues
(c) M/G/1 queue with server breakdown
Many new examples and exercises.
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