| Description |
1 online resource |
| Contents |
Cover; Title Page; Contents; Preface; Chapter 1. Discrete-Time Markov Chains; 1.1. Definitions and properties; 1.2. Strong Markov property; 1.3. Recurrent and transient states; 1.4. State classification; 1.5. Visits to a state; 1.6. State space decomposition; 1.7. Irreducible and recurrent Markov chains; 1.8. Aperiodic Markov chains; 1.9. Convergence to equilibrium; 1.10. Ergodic theorem; 1.11. First passage times and number of visits; 1.11.1. First passage time to a state; 1.11.2. First passage time to a subset of states; 1.11.3. Expected number of visits; 1.12. Finite Markov chains |
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1.13. Absorbing Markov chains1.14. Examples; 1.14.1. Two-state chain; 1.14.2. Gambler's ruin; 1.14.3. Success runs; 1.15. Bibliographical notes; Chapter 2. Continuous-Time Markov Chains; 2.1. Definitions and properties; 2.2. Transition functions and infinitesimal generator; 2.3. Kolmogorov's backward equation; 2.4. Kolmogorov's forward equation; 2.5. Existence and uniqueness of the solutions; 2.6. Recurrent and transient states; 2.7. State classification; 2.8. Explosion; 2.9. Irreducible and recurrent Markov chains; 2.10. Convergence to equilibrium; 2.11. Ergodic theorem |
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2.12. First passage times2.12.1. First passage time to a state; 2.12.2. First passage time to a subset of states; 2.13. Absorbing Markov chains; 2.14. Bibliographical notes; Chapter 3. Birth-and-Death Processes; 3.1. Discrete-time birth-and-death processes; 3.2. Absorbing discrete-time birth-and-death processes; 3.2.1. Passage times and convergence to equilibrium; 3.2.2. Expected number of visits; 3.3. Periodic discrete-time birth-and-death processes; 3.4. Continuous-time pure birth processes; 3.5. Continuous-time birth-and-death processes; 3.5.1. Explosion; 3.5.2. Positive recurrence |
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3.5.3. First passage time3.5.4. Explosive chain having an invariant probability; 3.5.5. Explosive chain without invariant probability; 3.5.6. Positive or null recurrent embedded chain; 3.6. Absorbing continuous-time birth-and-death processes; 3.6.1. Passage times and convergence to equilibrium; 3.6.2. Explosion; 3.7. Bibliographical notes; Chapter 4. Uniformization; 4.1. Introduction; 4.2. Banach spaces and algebra; 4.3. Infinite matrices and vectors; 4.4. Poisson process; 4.4.1. Order statistics; 4.4.2. Weighted Poisson distribution computation; 4.4.3. Truncation threshold computation |
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4.5. Uniformizable Markov chains4.6. First passage time to a subset of states; 4.7. Finite Markov chains; 4.8. Transient regime; 4.8.1. State probabilities computation; 4.8.2. First passage time distribution computation; 4.8.3. Application to birth-and-death processes; 4.9. Bibliographical notes; Chapter 5. Queues; 5.1. The M/M/1 queue; 5.1.1. State probabilities; 5.1.2. Busy period distribution; 5.2. The M/M/c queue; 5.3. The M/M/∞ queue; 5.4. Phase-type distributions; 5.5. Markovian arrival processes; 5.5.1. Definition and transient regime |
| Summary |
Markov chains are a fundamental class of stochastic processes. They are widely used to solve problems in a large number of domains such as operational research, computer science, communication networks and manufacturing systems. The success of Markov chains is mainly due to their simplicity of use, the large number of available theoretical results and the quality of algorithms developed for the numerical evaluation of many metrics of interest. The author presents the theory of both discrete-time and continuous-time homogeneous Markov chains. He carefully examines the explosion phenomen |
| Notes |
Print version record |
| Subject |
Markov processes.
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Markov processes
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| Form |
Electronic book
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| ISBN |
1299805175 |
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9781299805170 |
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9781118731444 |
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1118731441 |
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