MIT 6.262 Discrete Stochastic Processes, Spring 2011 (English)

   Saturday, December 21, 2024   

Starting a new Lecture Notes Series on MIT 6.262 Discrete Stochastic Processes, Spring 2011

Youtube Lecture Playlist Credits 

Channel Name: MIT OpenCourseWare

So Let Us Start to This Journey of Learning 

MIT 6.262 Discrete Stochastic Processes, Spring 2011 By Lecture Notes together!

Lecture 1: 1. Introduction and Probability Review

Lecture 2: 2. More Review; The Bernoulli Process

Lecture 3: 3. Law of Large Numbers, Convergence

Lecture 4: 4. Poisson (the Perfect Arrival Process)

Lecture 5: 5. Poisson Combining and Splitting

Lecture 6: 6. From Poisson to Markov

Lecture 7: 7. Finite-state Markov Chains; The Matrix Approach

Lecture 8: 8. Markov Eigenvalues and Eigenvectors

Lecture 9: 9. Markov Rewards and Dynamic Programming

Lecture 10: 10. Renewals and the Strong Law of Large Numbers

Lecture 11: 11. Renewals: Strong Law and Rewards

Lecture 12: 12. Renewal Rewards, Stopping Trials, and Wald's Inequality

Lecture 13: 13. Little, M/G/1, Ensemble Averages

Lecture 14: 14. Review

Lecture 15: 15. The Last Renewal

Lecture 16: 16. Renewals and Countable-state Markov

Lecture 17: 17. Countable-state Markov Chains

Lecture 18: 18. Countable-state Markov Chains and Processes

Lecture 19: 19. Countable-state Markov Processes

Lecture 20: 20. Markov Processes and Random Walks

Lecture 21: 21. Hypothesis Testing and Random Walks

Lecture 22: 22. Random Walks and Thresholds

Lecture 23: 23. Martingales (Plain, Sub, and Super)

Lecture 24: 24. Martingales: Stopping and Converging

Lecture 25: 25. Putting It All Together