*Textbook and Academic Authors Association (TAA) McGuffey Longevity Award Winner, 2024*

A trusted market leader for four decades, Sheldon Ross's Introduction to Probability Models offers a comprehensive foundation of this key subject with applications across engineering, computer science, management science, the physical and social sciences and operations research. Through its hallmark exercises and real examples, this valuable course text

Introduction to Probability Models provides the reader with a comprehensive course in the subject, from foundations to advanced topics.

Winner of a 2024 McGuffey Longevity Award (College) (Texty) from the Textbook and Academic Authors Association
Retains the useful organization that students and professors have relied on since 1972
Includes new coverage on Martingales
Offers a single source appropriate for a range of courses from undergraduate to graduate level

Produktinformation

Utgivningsdatum:2023-07-05
Mått:152 x 229 x 44 mm
Vikt:1 380 g
Format:Häftad
Språk:Engelska
Antal sidor:870
Upplaga:13
Förlag:Elsevier Science
ISBN:9780443187612

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Mer om författaren

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.

Innehållsförteckning

1. Introduction to Probability Theory2. Random Variables3. Conditional Probability and Conditional Expectation4. Markov Chains5. The Exponential Distribution and the Poisson Process6. Continuous-Time Markov Chains7. Renewal Theory and Its Applications8. Queueing Theory9. Reliability Theory10. Brownian Motion and Stationary Processes11. Simulation12. Coupling13. Martingales