| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 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 | Download |
| 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 |