- Inbunden (Hardback)
- Antal sidor
- Academic Press
- M.Ross, Sheldon
- 228 x 158 x 38 mm
- Antal komponenter
- 14:B&W 6 x 9 in or 229 x 152 mm Case Laminate on White w/Gloss Lam
- 1133 g
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Introduction to Probability Models
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The hallmark features of this text have been retained in this eleventh edition: superior writing style; excellent exercises and examples covering the wide breadth of coverage of probability topic; and real-world applications in engineering, science, business and economics. The 65% new chapter material includes coverage of finite capacity queues, insurance risk models, and Markov chains, as well as updated data. The book contains compulsory material for new Exam 3 of the Society of Actuaries including several sections in the new exams. It also presents new applications of probability models in biology and new material on Point Processes, including the Hawkes process. There is a list of commonly used notations and equations, along with an instructor's solutions manual.
This text will be a helpful resource for professionals and students in actuarial science, engineering, operations research, and other fields in applied probability.
- Updated data, and a list of commonly used notations and equations, instructor's solutions manual
- Offers new applications of probability models in biology and new material on Point Processes, including the Hawkes process
- Introduces elementary probability theory 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
- Covers finite capacity queues, insurance risk models, and Markov chains
- Contains compulsory material for new Exam 3 of the Society of Actuaries including several sections in the new exams
- Appropriate for a full year course, this book is written under the assumption that students are familiar with calculus
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"The hallmark features of this renowned text remain in this eleventh edition: superior writing style; excellent exercises and examples covering the wide breadth of coverage of probability topic; and real-world applications in engineering, science, business and economics.new chapter material includes coverage of finite capacity queues, insurance risk models, and Markov chains, as well as updated data." --Zentralblatt MATH 1284-1
".the newest edition updated with new examples and exercises, actuarial material, Hawkes and other point processes, Brownian motion, and expanded coverage of Markov chains. Although formally rigorous, the emphasis is on helping students to develop an intuitive sense for probabilistic thinking."--ProtoView.com, April 2014
Praise from Reviewers for the 10th edition: "I think Ross has done an admirable job of covering the breadth of applied probability. Ross writes fantastic problems which really force the students to think divergently...The examples, like the exercises are great." --Matt Carlton, California Polytechnic Institute "This is a fascinating introduction to applications from a variety of disciplines. Any curious student will love this book." --Jean LeMaire, University of Pennsylvania "This book may be a model in the organization of the education process. I would definitely rate this text to be the best probability models book at its level of difficulty...far more sophisticated and deliberate than its competitors." --Kris Ostaszewski, University of Illinois
Bloggat om Introduction to Probability Models
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.
Preface 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 Appendix: Solutions to Starred Exercises Index