Learn · Discrete Stochastic Processes

Lecture 12 of 25Video Lectures1 h 27 min

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

Prof. Robert Gallager · Massachusetts Institute of Technology

At MIT OpenCourseWare Transcript

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From
Discrete Stochastic Processes, Massachusetts Institute of Technology (at MIT OpenCourseWare)
Licence
CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only.
Attribution
Prof. Robert Gallager. Discrete Stochastic Processes. Spring 2011. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
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All the lectures

  1. Lecture 1: Introduction and Probability Review 1 h 17 min · Video
  2. Lecture 2: More Review; The Bernoulli Process 1 h 9 min · Video
  3. Lecture 3: Law of Large Numbers, Convergence 1 h 22 min · Video
  4. Lecture 4: Poisson (The Perfect Arrival Process) 1 h 18 min · Video
  5. Lecture 5: Poisson Combining and Splitting 1 h 25 min · Video
  6. Lecture 6: From Poisson to Markov 1 h 20 min · Video
  7. Lecture 7: Finite-state Markov Chains; The Matrix Approach 56 min · Video
  8. Lecture 8: Markov Eigenvalues and Eigenvectors 1 h 24 min · Video
  9. Lecture 9: Markov Rewards and Dynamic Programming 1 h 24 min · Video
  10. Lecture 10: Renewals and the Strong Law of Large Numbers 1 h 22 min · Video
  11. Lecture 11: Renewals: Strong Law and Rewards 1 h 19 min · Video
  12. Lecture 12: Renewal Rewards, Stopping Trials, and Wald's Inequality 1 h 27 min · Video
  13. Lecture 13: Little, M/G/1, Ensemble Averages 1 h 15 min · Video
  14. Lecture 14: Review 1 h 20 min · Video
  15. Lecture 15: The Last Renewal 1 h 16 min · Video
  16. Lecture 16: Renewals and Countable-state Markov 1 h 20 min · Video
  17. Lecture 17: Countable-state Markov Chains 1 h 24 min · Video
  18. Lecture 18: Countable-state Markov Chains and Processes 1 h 17 min · Video
  19. Lecture 19: Countable-state Markov Processes 1 h 23 min · Video
  20. Lecture 20: Markov Processes and Random Walks 1 h 24 min · Video
  21. Lecture 21: Hypothesis Testing and Random Walks 1 h 26 min · Video
  22. Lecture 22: Random Walks and Thresholds 1 h 22 min · Video
  23. Lecture 23: Martingales (Plain, Sub, and Super) 1 h 23 min · Video
  24. Lecture 24: Martingales: Stopping and Converging 1 h 21 min · Video
  25. Lecture 25: Putting It All Together 1 h 22 min · Video