Computational Probability
Chapter 3 discusses how to find transient probabilities and transient rewards for these Markov chains. The next two chapters indicate how to find steady-state probabilities for Markov chains with a finite number of states. Both direct and iterative methods are described in Chapter 4. Details of thes...
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Springer US
2000, 2000
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| Edition: | 1st ed. 2000 |
| Series: | International Series in Operations Research & Management Science
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| Subjects: | |
| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1 Computational Probability: Challenges and Limitations
- 2 Tools for Formulating Markov Models
- 3 Transient Solutions for Markov Chains
- 4 Numerical Methods for Computing Stationary Distributions of Finite Irreducible Markov Chains
- 5 Stochastic Automata Networks
- 6 Matrix Analytic Methods
- 7 Use of Characteristic Roots for Solving Infinite State Markov Chains
- 8 An Introduction to Numerical Transform Inversion and Its Application to Probability Models
- 9 Optimal Control of Markov Chains
- 10 On Numerical Computations of Some Discrete-Time Queues
- 11 The Product Form Tool for Queueing Networks
- 12 Techniques for System Dependability Evaluation