Learn · Introduction to Stochastic Processes

Lecture 43 of 50Week 11

Lecture 43: Introduction to renewal Theory

Prof. Manjesh Hanawal · IIT Bombay

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From
Introduction to Stochastic Processes, IIT Bombay (at NPTEL)
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CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
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All the lectures

  1. Lecture 1: Sample Space and events Video
  2. Lecture 2: Axioms of Probability Video
  3. Lecture 3: Independence of events and Conditional Probability Video
  4. Lecture 4: Baye's Theorem and Introduction to Random Variables Video
  5. Lecture 5: CDF and it's properties Video
  6. Lecture 6: Continuity of Probability Video
  7. Lecture 7: Discrete and Continuous random variables Video
  8. Lecture 8: Expectation of random variables and its properties Video
  9. Lecture 9: Variance and some inequalities of random variables Video
  10. Lecture 10: Discrete Probability Distributions Video
  11. Lecture 11: Continuous Probability Distributions Video
  12. Lecture 12: Jointly distributed random variables and conditional distributions Video
  13. Lecture 13: Correlation and Covariance Video
  14. Lecture 14: Transformation of random vectors Video
  15. Lecture 15: Gaussian random vector and joint Gaussian distribution Video
  16. Lecture 16: Random Processes Video
  17. Lecture 17: Properties of random Process Video
  18. Lecture 18: Poisson Process Video
  19. Lecture 19: Properties of Poisson Process (Part 1) Video
  20. Lecture 20: Properties of Poisson Process (Part 2) Video
  21. Lecture 21: Convergence of sequence of random variables (Part 1) Video
  22. Lecture 22: Convergence of sequence of random variables (Part 2) Video
  23. Lecture 23: Relation between different notions of convergence Video
  24. Lecture 24: Cauchy's criteria of convergence Video
  25. Lecture 25: Convergence in expectation Video
  26. Lecture 26: Law of Large Numbers Video
  27. Lecture 27: Central limit theorem Video
  28. Lecture 28: chernoff bound Video
  29. Lecture 29: Introduction to Markov property Video
  30. Lecture 30: Transition Probability Matrix Video
  31. Lecture 31: Finite dimensional distribution of Markov chains Video
  32. Lecture 32: Strong Markov Property Video
  33. Lecture 33: Stopping Time Video
  34. Lecture 34: Hitting Times and Recurrence Video
  35. Lecture 35: Mean Number of returns to a state Video
  36. Lecture 36: Communicating classes and class properties Video
  37. Lecture 37: Class Properties Continued Video
  38. Lecture 38: Positive Recurrence and The Invariant Probability Vector Video
  39. Lecture 39: Properties of Invariant Probability Vector Video
  40. Lecture 40: Condition For Transience Video
  41. Lecture 41: Example of Queue Video
  42. Lecture 42: Queue Continued and Example of Page Rank Video
  43. Lecture 43: Introduction to renewal Theory Video
  44. Lecture 44: The Elementary Renewal Theorem Video
  45. Lecture 45: Application to DTMC Video
  46. Lecture 46: Renewal Reward Theorem Video
  47. Lecture 47: Introduction to Continuous Time Markov Chains Video
  48. Lecture 48: Properties of states in CTMC Video
  49. Lecture 49: Embedded markov chain Video
  50. Live Session 18-02-2020 Video