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7. Finite-state Markov Chains; The Matrix Approach
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Classroom Contents
Discrete Stochastic Processes
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- 1 1. Introduction and Probability Review
- 2 2. More Review; The Bernoulli Process
- 3 3. Law of Large Numbers, Convergence
- 4 4. Poisson (the Perfect Arrival Process)
- 5 5. Poisson Combining and Splitting
- 6 6. From Poisson to Markov
- 7 7. Finite-state Markov Chains; The Matrix Approach
- 8 8. Markov Eigenvalues and Eigenvectors
- 9 9. Markov Rewards and Dynamic Programming
- 10 10. Renewals and the Strong Law of Large Numbers
- 11 11. Renewals: Strong Law and Rewards
- 12 12. Renewal Rewards, Stopping Trials, and Wald's Inequality
- 13 13. Little, M/G/1, Ensemble Averages
- 14 14. Review
- 15 15. The Last Renewal
- 16 16. Renewals and Countable-state Markov
- 17 17. Countable-state Markov Chains
- 18 18. Countable-state Markov Chains and Processes
- 19 19. Countable-state Markov Processes
- 20 20. Markov Processes and Random Walks
- 21 21. Hypothesis Testing and Random Walks
- 22 22. Random Walks and Thresholds
- 23 23. Martingales (Plain, Sub, and Super)
- 24 24. Martingales: Stopping and Converging
- 25 25. Putting It All Together
