Introduction to Probability Models (13th Ed.)
Auteur : Ross Sheldon M.
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
- Awarded the 2020 Textbook Excellence Award (Texty) from the Textbook and Academic Authors Association (prior edition)
- 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