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Learn · Measure Theoretic Probability 1

Lecture 16 of 40Week 4: Law and Distribution Function of a random variable

Lecture 16: Law or Distribution of an RV

Prof. Suprio Bhar · IIT Kanpur

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Measure Theoretic Probability 1, IIT Kanpur (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 01: Introduction to the course Measure Theoretic Probability 1 YouTube
  2. Lecture 02: Sigma-fields and Measurable spaces YouTube
  3. Lecture 03: Fields and Generating sets for Sigma-fields YouTube
  4. Lecture 04: Borel Sigma-field on R and other sets YouTube
  5. Lecture 05: Limits of sequences of sets and Monotone classes YouTube
  6. Lecture 06: Measures and Measure Spaces YouTube
  7. Lecture 07: Probability Measures YouTube
  8. Lecture 08: Properties of Measures (Part 1) YouTube
  9. Lecture 09: Properties of Measures (Part 2) YouTube
  10. Lecture 10: Properties of Measures (Part 3) YouTube
  11. Lecture 11: Measurable functions YouTube
  12. Lecture 12: Borel measurable functions YouTube
  13. Lecture 13: Algebraic properties of Measurable functions YouTube
  14. Lecture 14: Limiting behaviour of measurable functions YouTube
  15. Lecture 15: Random Variables and Random Vectors YouTube
  16. Lecture 16: Law or Distribution of an RV YouTube
  17. Lecture 17: Distribution Function of an RV YouTube
  18. Lecture 18: Decomposition of Distribution functions YouTube
  19. Lecture 19: Construction of RVs with a specified law YouTube
  20. Lecture 20: Caratheodery Extension Theorem YouTube
  21. Lecture 21: From Distribution Functions to Probability Measures (Part 1) YouTube
  22. Lecture 22: From Distribution Functions to Probability Measures (Part 2) YouTube
  23. Lecture 23: Lebesgue-Stieltjes Measures YouTube
  24. Lecture 24: Properties of Lebesgue Measure on R YouTube
  25. Lecture 25: Distribution Functions and Probability Measures in higher dimensions YouTube
  26. Lecture 26: Integration of measurable functions YouTube
  27. Lecture 27: Properties of Measure Theoretic Integration (Part 1) YouTube
  28. Lecture 28: Properties of Measure Theoretic Integration (Part 2) YouTube
  29. Lecture 29: Monotone Convergence Theorem YouTube
  30. Lecture 30: Computation of Expectation for Discrete RVs YouTube
  31. Lecture 31: MCT and the Linearity of Measure Theoretic Integration YouTube
  32. Lecture 32: Sets of measure zero and Measure Theoretic Integration YouTube
  33. Lecture 33: Fatou's Lemma and Dominated Convergence Theorem YouTube
  34. Lecture 34: Riemann and Lebesgue integration YouTube
  35. Lecture 35: Computations involving Lebesgue Integration YouTube
  36. Lecture 36: Decomposition of Measures YouTube
  37. Lecture 37: Absolutely Continuous RVs YouTube
  38. Lecture 38: Expectation of Absolutely Continuous RVs YouTube
  39. Lecture 39: Inequalities involving moments of RVs YouTube
  40. Lecture 40: Conclusion to the course Measure Theoretic Probability 1 YouTube