Probability & Random Variables

Probability & Random Variables
Lecture Series on Probability and Random Variables by Prof. M. Chakraborty, Department of Electronics and Electrical Communication Engineering, I.I.T.,Kharagpur
Lecture - 1 Introduction to the Theory of Probability
Lecture - 2 Axioms of Probability
Lecture - 3 Axioms of Probability (Contd.)
Lecture - 4 Introduction to Random Variables
Lecture - 5 Probability Distributions and Density Functions
Lecture - 6 Conditional Distribution and Density Functions
Lecture - 7 Function of a Random Variable
Lecture - 8 Function of a Random Variable (Contd.)
Lecture - 9 Mean and Variance of a Random Variable
Lecture - 10 Moments
Lecture - 11 Characteristic Function
Lecture - 12 Two Random Variables
Lecture - 13 Function of Two Random Variables
Lecture - 14 Function of Two Random Variables (Contd.)
Lecture - 15 Correlation Covariance and Related Innver
Lecture - 16 Vector Space of Random Variables
Lecture - 17 Joint Moments
Lecture - 18 Joint Characteristic Functions
Lecture - 19 Joint Conditional Densities
Lecture - 20 Joint Conditional Densities (Contd.)
Lecture - 21 Sequences of Random Variables
Lecture - 22 Sequences of Random Variables (Contd.)
Lecture - 23 Correlation Matrices and their Properties
Lecture - 24 Correlation Matrices and their Properties
Lecture - 25 Conditional Densities of Random Vectors
Lecture - 26 Characteristic Functions and Normality
Lecture - 27 Thebycheff Inquality and Estimation
Lecture - 28 Central Limit Theorem
Lecture - 29 Introduction to Stochastic Process
Lecture - 30 Stationary Processes
Lecture - 31 Cyclostationary Processes
Lecture - 32 System with Random Process at Input
Lecture - 33 Ergodic Processes
Lecture - 34 Introduction to Spectral Analysis
Lecture - 35 Spectral Analysis Contd.
Lecture - 36 Spectrum Estimation - Non Parametric Methods
Lecture - 37 Spectrum Estimation - Parametric Methods
Lecture - 38 Autoregressive Modeling and Linear Prediction
Lecture - 39 Linear Mean Square Estimation - Wiener (FIR)