Modules / Lectures

Module Name | Download |
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Sl.No | Chapter Name | MP4 Download |
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1 | Lecture 1: Sample Space and events | Download |

2 | Lecture 2: Axioms of Probability | Download |

3 | Lecture 3: Independence of events and Conditional Probability | Download |

4 | Lecture 4: Baye’s Theorem and Introduction to Random Variables | Download |

5 | Lecture 5: CDF and it’s properties | Download |

6 | Lecture 6: Continuity of Probability | Download |

7 | Lecture 7: Discrete and Continuous random variables | Download |

8 | Lecture 8: Expectation of random variables and its properties | Download |

9 | Lecture 9: Variance and some inequalities of random variables | Download |

10 | Lecture 10: Discrete Probability Distributions | Download |

11 | Lecture 11: Continuous Probability Distributions | Download |

12 | Lecture 12: Jointly distributed random variables and conditional distributions | Download |

13 | Lecture 13: Correlation and Covariance | Download |

14 | Lecture 14: Transformation of random vectors | Download |

15 | Lecture 15: Gaussian random vector and joint Gaussian distribution | Download |

16 | Lecture 16: Random Processes | Download |

17 | Lecture 17: Properties of random Process | Download |

18 | Lecture 18: Poisson Process | Download |

19 | Lecture 19: Properties of Poisson Process (Part 1) | Download |

20 | Lecture 20: Properties of Poisson Process (Part 2) | Download |

21 | Lecture 21: Convergence of sequence of random variables (Part 1) | Download |

22 | Lecture 22: Convergence of sequence of random variables (Part 2) | Download |

23 | Lecture 23: Relation between different notions of convergence | Download |

24 | Lecture 24: Cauchy’s criteria of convergence | Download |

25 | Lecture 25: Convergence in expectation | Download |

26 | Lecture 26: Law of Large Numbers | Download |

27 | Lecture 27: Central limit theorem | Download |

28 | Lecture 28: chernoff bound | Download |

29 | Lecture 29: Introduction to Markov property | Download |

30 | Lecture 30: Transition Probability Matrix | Download |

31 | Lecture 31: Finite dimensional distribution of Markov chains | Download |

32 | Lecture 32: Strong Markov Property | Download |

33 | Lecture 33: Stopping Time | Download |

34 | Lecture 34: Hitting Times and Recurrence | Download |

35 | Lecture 35: Mean Number of returns to a state | Download |

36 | Lecture 36: Communicating classes and class properties | Download |

37 | Lecture 37: Class Properties Continued | Download |

38 | Lecture 38: Positive Recurrence and The Invariant Probability Vector | Download |

39 | Lecture 39: Properties of Invariant Probability Vector | Download |

40 | Lecture 40: Condition For Transience | Download |

41 | Lecture 41: Example of Queue | Download |

42 | Lecture 42: Queue Continued and Example of Page Rank | Download |

43 | Lecture 43: Introduction to renewal Theory | Download |

44 | Lecture 44: The Elementary Renewal Theorem | Download |

45 | Lecture 45: Application to DTMC | Download |

46 | Lecture 46: Renewal Reward Theorem | Download |

47 | Lecture 47: Introduction to Continuous Time Markov Chains | Download |

48 | Lecture 48: Properties of states in CTMC | Download |

49 | Lecture 49: Embedded markov chain | Download |

Sl.No | Chapter Name | English |
---|---|---|

1 | Lecture 1: Sample Space and events | Download Verified |

2 | Lecture 2: Axioms of Probability | Download Verified |

3 | Lecture 3: Independence of events and Conditional Probability | Download Verified |

4 | Lecture 4: Baye’s Theorem and Introduction to Random Variables | Download Verified |

5 | Lecture 5: CDF and it’s properties | Download Verified |

6 | Lecture 6: Continuity of Probability | Download Verified |

7 | Lecture 7: Discrete and Continuous random variables | Download Verified |

8 | Lecture 8: Expectation of random variables and its properties | Download Verified |

9 | Lecture 9: Variance and some inequalities of random variables | Download Verified |

10 | Lecture 10: Discrete Probability Distributions | Download Verified |

11 | Lecture 11: Continuous Probability Distributions | Download Verified |

12 | Lecture 12: Jointly distributed random variables and conditional distributions | Download Verified |

13 | Lecture 13: Correlation and Covariance | Download Verified |

14 | Lecture 14: Transformation of random vectors | Download Verified |

15 | Lecture 15: Gaussian random vector and joint Gaussian distribution | Download Verified |

16 | Lecture 16: Random Processes | Download Verified |

17 | Lecture 17: Properties of random Process | Download Verified |

18 | Lecture 18: Poisson Process | Download Verified |

19 | Lecture 19: Properties of Poisson Process (Part 1) | Download Verified |

20 | Lecture 20: Properties of Poisson Process (Part 2) | Download Verified |

21 | Lecture 21: Convergence of sequence of random variables (Part 1) | Download Verified |

22 | Lecture 22: Convergence of sequence of random variables (Part 2) | Download Verified |

23 | Lecture 23: Relation between different notions of convergence | Download Verified |

24 | Lecture 24: Cauchy’s criteria of convergence | Download Verified |

25 | Lecture 25: Convergence in expectation | Download Verified |

26 | Lecture 26: Law of Large Numbers | Download Verified |

27 | Lecture 27: Central limit theorem | Download Verified |

28 | Lecture 28: chernoff bound | Download Verified |

29 | Lecture 29: Introduction to Markov property | Download Verified |

30 | Lecture 30: Transition Probability Matrix | Download Verified |

31 | Lecture 31: Finite dimensional distribution of Markov chains | Download Verified |

32 | Lecture 32: Strong Markov Property | Download Verified |

33 | Lecture 33: Stopping Time | Download Verified |

34 | Lecture 34: Hitting Times and Recurrence | Download Verified |

35 | Lecture 35: Mean Number of returns to a state | Download Verified |

36 | Lecture 36: Communicating classes and class properties | Download Verified |

37 | Lecture 37: Class Properties Continued | Download Verified |

38 | Lecture 38: Positive Recurrence and The Invariant Probability Vector | Download Verified |

39 | Lecture 39: Properties of Invariant Probability Vector | Download Verified |

40 | Lecture 40: Condition For Transience | Download Verified |

41 | Lecture 41: Example of Queue | Download Verified |

42 | Lecture 42: Queue Continued and Example of Page Rank | Download Verified |

43 | Lecture 43: Introduction to renewal Theory | Download Verified |

44 | Lecture 44: The Elementary Renewal Theorem | Download Verified |

45 | Lecture 45: Application to DTMC | Download Verified |

46 | Lecture 46: Renewal Reward Theorem | Download Verified |

47 | Lecture 47: Introduction to Continuous Time Markov Chains | Download Verified |

48 | Lecture 48: Properties of states in CTMC | Download Verified |

49 | Lecture 49: Embedded markov chain | Download Verified |

Sl.No | Language | Book link |
---|---|---|

1 | English | Not Available |

2 | Bengali | Not Available |

3 | Gujarati | Not Available |

4 | Hindi | Not Available |

5 | Kannada | Not Available |

6 | Malayalam | Not Available |

7 | Marathi | Not Available |

8 | Tamil | Not Available |

9 | Telugu | Not Available |