Probability and Random Processes (English)

   Wednesday, April 10, 2024   

Starting a new Lecture Notes Series on Probability and Random Processes

Youtube Lecture Playlist Credits 

Channel Name: NPTEL IIT Guwahati

So Let Us Start to This Journey of Learning 

Probability and Random Processes By Lecture Notes together!

Lecture 1: Probability and Random Processes

Lecture 2: Lec 1: Introduction

Lecture 3: Lec 2: Algebra of Events

Lecture 4: Lec 3: Axioms of Probability

Lecture 5: Lec 4: Example 1

Lecture 6: Lec 5: Example 2

Lecture 7: Lec 6: Example 3

Lecture 8: Lec 7: Example 4

Lecture 9: Lec 8: Example 5

Lecture 10: Lec 9: Conditional Probability

Lecture 11: Lec 10: Bayes Theorem 1

Lecture 12: Lec 11: Bayes Theorem 2

Lecture 13: Lec 12: A Brief Review

Lecture 14: Lec 13: Example 1

Lecture 15: Lec 14: Example 2

Lecture 16: Lec 15: Example 3

Lecture 17: Lec 16: Example 4

Lecture 18: Lec 17: Example 5

Lecture 19: Lec 18: Independent Events

Lecture 20: Lec 19: A Brief Review

Lecture 21: Lec 20: Example 1

Lecture 22: Lec 21: Example 2

Lecture 23: Lec 22: Example 3

Lecture 24: Lec 23: Example 4

Lecture 25: Lec 24: Discrete Random Variables

Lecture 26: Lec 25: Expectation

Lecture 27: Lec 26: Moments

Lecture 28: Lec 27: Variance

Lecture 29: Lec 28: Binomial Random Variables

Lecture 30: Lec 29: Poisson Random Variables

Lecture 31: Lec 30: More on Poission Random Variables

Lecture 32: Lec 31: Properties of the CDF

Lecture 33: Lec 32: A Brief Review I

Lecture 34: Lec 33: A Brief Review II

Lecture 35: Lec 34: Example 1

Lecture 36: Lec 35: Example 2

Lecture 37: Lec 36: Example 3

Lecture 38: Lec 37: Example 4

Lecture 39: Lec 38: Example 5

Lecture 40: Lec 39: Example 6

Lecture 41: Lec 40: Example 7

Lecture 42: Lec 41: Example 8

Lecture 43: Lec 42: Example 9

Lecture 44: Lec 43: Continuous Random Variables

Lecture 45: Lec 44: Expectation of Continuous random variables

Lecture 46: Lec 45: The uniform and the Gaussian Random variables

Lecture 47: Lec 46: The mean and variance of a Gaussian Random Variable

Lecture 48: Lec 47: The exponential random variable and other continuous distributions

Lecture 49: Lec 48: A Brief Review

Lecture 50: Lec 49: Example 1

Lecture 51: Lec 50: Example 2

Lecture 52: Lec 51: Example 3

Lecture 53: Lec 52: Example 4

Lecture 54: Lec 53: Example 5

Lecture 55: Lec 54: Functions of a random varible

Lecture 56: Lec 55: Functions of a random varible

Lecture 57: Lec 56: The moment generating function

Lecture 58: Lec 57: Conditional Distributions

Lecture 59: Lec 58: Bivariate Distributions

Lecture 60: Lec 59: Independence of Random Varibles

Lecture 61: Lec 60: Jointly Gaussian Random Varibales and Circular symmetry

Lecture 62: Lec 61: Jointly Discrete Random Variables

Lecture 63: Lec 62: One Function of two random variables

Lecture 64: Lec 63: Order Statistics

Lecture 65: Lec 64: Two functions of two random variables

Lecture 66: Lec 65: Joint Moments

Lecture 67: Lec 66: Joint Charactristic Functions

Lecture 68: Lec 67: Conditional Distributions for multiple random variables

Lecture 69: Lec 68: Conditional Expectations

Lecture 70: Lec 69: Examples

Lecture 71: Lec 70: Random Vectors

Lecture 72: Lec 71: Independence of Random Varibles

Lecture 73: Lec 72: Complex Random Varibales

Lecture 74: Lec 73: Covariance Matrices

Lecture 75: Lec 74: Conditional Densities

Lecture 76: Lec 75: Gaussianity

Lecture 77: Lec 76: Chi Squared Densities

Lecture 78: Lec 77: Examples

Lecture 79: Lec 78: Estimation Theory

Lecture 80: Lec 79: Measurements

Lecture 81: Lec 80: Sequences of Random Variables

Lecture 82: Lec 81: Laws of large numbers

Lecture 83: Lec 82: Random processes

Lecture 84: Lec 83: Stationarity, Cyclostationarity, Ergodicity

Lecture 85: Lec 84: Random Processes as Signals (PSD and LTI Response)

Lecture 86: Lec 85: White and Gaussian Processes Noise

Lecture 87: Lec 87: Examples

Lecture 88: Lec 86: Examples