P1. 1. Introduction and Probability Review.flv

P2. 2. More Review; The Bernoulli Process.flv

P3. 3. Law of Large Numbers, Convergence.flv

P4. 4. Poisson the Perfect Arrival Process.flv

P5. 5. Poisson Combining and Splitting.flv

P6. 6. From Poisson to Markov.flv

P7. 7. Finite-state Markov Chains; The Matrix Approach.flv

P8. 8. Markov Eigenvalues and Eigenvectors.flv

P9. 9. Markov Rewards and Dynamic Programming.flv

P9. 9. Markov Rewards and Dynamic Programming[00].mp4

P9. 9. Markov Rewards and Dynamic Programming[01].mp4

P10. 10. Renewals and the Strong Law of Large Numbers.flv

P11. 11. Renewals  Strong Law and Rewards.flv

P12. 12. Renewal Rewards, Stopping Trials, and Wald s Inequal.flv

P13. 13. Little, M G 1, Ensemble Averages.flv

P14. 14. Review.flv

P15. 15. The Last Renewal.flv

P16. 16. Renewals and Countable-state Markov.flv

P17. 17. Countable-state Markov Chains.flv

P18. 18. Countable-state Markov Chains and Processes.flv

P19. 19. Countable-state Markov Processes.flv

P20. 20. Markov Processes and Random Walks.flv

P21. 21. Hypothesis Testing and Random Walks.flv

P22. 22. Random Walks and Thresholds.flv

P23. 23. Martingales Plain, Sub, and Super.flv

P24. 24. Martingales  Stopping and Converging.flv

P25. 25. Putting It All Together.flv