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. 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
With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries.
This important text: Includes classroom-tested problems and solutions to probability exercises Highlights real-world exercises designed to make clear the concepts presented Uses Mathematica software to illustrate the text’s computer ...
Introduction to Probability Models, Student Solutions Manual (e-only)
With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries.
This strategy was first articulated by Kelly (1956). Breiman (1961) showed that if you want to maximise the long-term growth rate, or to minimise the mean time to reach a desired level of capital, you cannot improve on betting a ...
This is the currently used textbook for an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students, and for a leading online class on the subject.
本书主要内容有随机变量,条件期望,马尔可夫链,指数分布,泊松过程,平稳过程,更新理论及排队论等,也包括了随机过程在物理,生物,运筹,网络,遗传,经济,保险,金融及可靠性中的应用.
Concise advanced-level introduction to stochastic processes that arise in applied probability. Poisson process, renewal theory, Markov chains, Brownian motion, much more. Problems. References. Bibliography. 1970 edition.
Reviews fundamental concepts and applications of probability and statistics. After a general overview, it considers special types of random variables, using examples which illustrate their wide variety of applications. Also...
A key feature of this book is its many interesting examples and exercises that have been chosen to illuminate the techniques presented.