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Elsevier

Introduction to Probability Models

  • 13 Edición - 30 de junio de 2023
  • Última edición
  • Autor: Sheldon M. Ross
  • Idioma: Inglés

*Textbook and Academic Authors Association (TAA) McGuffey Longevity Award Winner, 2024*A trusted market leader for four decades, Sheldon Ross’s Introduction to Probability M… Leer más

Descripción

*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.

Puntos claves

  • 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

De interès para

Undergraduate students of all backgrounds in introduction to probability modelling courses, typically in Math or Statistics departments

Índice

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

Detalles del producto

  • Edición: 13
  • Última edición
  • Publicado: 5 de julio de 2023
  • Idioma: Inglés

Sobre el autor

SR

Sheldon M. Ross

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.
Afiliaciones y experiencia
Professor, Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, USA

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