Introduction to Probability Models (13th Ed.)
Auteur : Ross Sheldon M.
*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.
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
- 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
Date de parution : 07-2023
Ouvrage de 870 p.
15.2x22.8 cm
Mots-clés :
Alias Method; Antithetic Variables; Balance Equations; Birth and Death Models; Birth and Death Process; Bounds; Branching Processes; Brownian Motion; Busy Period; Conditioning; Continuous Time Markov Chain; Control Variables; Covariance Function; Erlang Loss System; Fourier Transforms; Hidden Markov Chains; IFR; IFRA; Importance Sampling; Inverse Transform Method; Limiting Probabilities; M/G/1; M/M/1; Markov Chain; Markov Chain Monte Carlo; Markov Decision Processes; Martingale; Maximum Variable; Minimal Cut Set; Minimal Path Set; Multi Server; Networks; Option Pricing; Polar Method; Priorities; Queues; Rejection Method; Reliability Function; Stationary Probabilities; Structure Function; Time Reversibility; Transition Probabilities; Variance Reduction; White Noise; Wiener Process