Sheldon M. Ross

Introduction to Probability Models (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 842
• Erscheinungstermin: 9. März 2019
• Englisch
• ISBN-13: 9780128143476
• Artikelnr.: 55800976

Autorenporträt

Dr. Sheldon M. Ross is a professor in the Department of Industrial and Systems Engineering at the University of Southern California. He received his PhD 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, a Fellow of INFORMS, and a recipient of the Humboldt US Senior Scientist Award.

Inhaltsangabe

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
13. Martingales