MIT 6.262 Discrete Stochastic Processes, Spring 2011
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What you'll learn
This course includes
- 33 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 25 lessons • 33 hours of video
MIT 6.262 Discrete Stochastic Processes, Spring 2011
25 lessons
• 33 hours
MIT 6.262 Discrete Stochastic Processes, Spring 2011
25 lessons
• 33 hours
- 1. Introduction and Probability Review 01:16:27
- 2. More Review; The Bernoulli Process 01:08:20
- 3. Law of Large Numbers, Convergence 01:21:28
- 4. Poisson (the Perfect Arrival Process) 01:17:14
- 5. Poisson Combining and Splitting 01:24:32
- 6. From Poisson to Markov 01:19:17
- 7. Finite-state Markov Chains; The Matrix Approach 55:34
- 8. Markov Eigenvalues and Eigenvectors 01:23:38
- 9. Markov Rewards and Dynamic Programming 01:23:36
- 10. Renewals and the Strong Law of Large Numbers 01:21:53
- 11. Renewals: Strong Law and Rewards 01:18:17
- 12. Renewal Rewards, Stopping Trials, and Wald's Inequality 01:26:21
- 13. Little, M/G/1, Ensemble Averages 01:14:53
- 14. Review 01:19:19
- 15. The Last Renewal 01:15:44
- 16. Renewals and Countable-state Markov 01:19:40
- 17. Countable-state Markov Chains 01:23:46
- 18. Countable-state Markov Chains and Processes 01:16:29
- 19. Countable-state Markov Processes 01:22:14
- 20. Markov Processes and Random Walks 01:23:09
- 21. Hypothesis Testing and Random Walks 01:25:23
- 22. Random Walks and Thresholds 01:21:17
- 23. Martingales (Plain, Sub, and Super) 01:22:40
- 24. Martingales: Stopping and Converging 01:20:44
- 25. Putting It All Together 01:21:27
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