Probability & Random Variables

Course Description

This course, Probability & Random Variables, has 40 video lectures with Professor M. Chakraborty, Department of Electronics and Electrical Communication Engineering, I.I.T., Kharagpur.

Probability & Random Variables
Prof. M. Chakraborty on Lecture 8, Function of a Random Variable (Cont.).
4 ratings

Video Lectures & Study Materials

# Lecture Play Lecture
1 Introduction to the Theory of Probability (59:49) Play Video
2 Axioms of Probability (59:49) Play Video
3 Axioms of Probability (Contd.) (59:51) Play Video
4 Introduction to Random Variables (59:37) Play Video
5 Probability Distributions and Density Functions (59:48) Play Video
6 Conditional Distribution and Density Functions (59:55) Play Video
7 Function of a Random Variable (59:53) Play Video
8 Function of a Random Variable (Contd.) (59:50) Play Video
9 Mean and Variance of a Random Variable (59:54) Play Video
10 Moments (59:47) Play Video
11 Characteristic Function (59:55) Play Video
12 Two Random Variables (59:53) Play Video
13 Function of Two Random Variables (01:02:50) Play Video
14 Function of Two Random Variables (Contd.) (59:46) Play Video
15 Correlation Covariance and Related Innver (59:54) Play Video
16 Vector Space of Random Variables (59:47) Play Video
17 Joint Moments (59:54) Play Video
18 Joint Characteristic Functions (59:48) Play Video
19 Joint Conditional Densities (59:49) Play Video
20 Joint Conditional Densities (Contd.) (59:57) Play Video
21 Sequences of Random Variables (59:57) Play Video
22 Sequences of Random Variables (Contd.) (59:44) Play Video
23 Correlation Matrices and their Properties (59:53) Play Video
24 Correlation Matrices and their Properties (59:50) Play Video
25 Conditional Densities of Random Vectors (59:56) Play Video
26 Characteristic Functions and Normality (01:00:58) Play Video
27 Thebycheff Inquality and Estimation (59:55) Play Video
28 Central Limit Theorem (59:55) Play Video
29 Introduction to Stochastic Process (59:55) Play Video
30 Stationary Processes (59:49) Play Video
31 Cyclostationary Processes (59:52) Play Video
32 System with Random Process at Input (01:00:01) Play Video
33 Ergodic Processes (59:57) Play Video
34 Introduction to Spectral Analysis (59:51) Play Video
35 Spectral Analysis Contd. (59:57) Play Video
36 Spectrum Estimation - Non Parametric Methods (59:50) Play Video
37 Spectrum Estimation - Parametric Methods (01:00:03) Play Video
38 Autoregressive Modeling and Linear Prediction (59:49) Play Video
39 Linear Mean Square Estimation - Wiener (FIR) (59:57) Play Video
40 Adaptive Filtering - LMS Algorithm (59:47) Play Video

Comments

Displaying 2 comments:

sathiya wrote 8 years ago.
I need material in advanced digital signal processing

Manish Agarwal wrote 9 years ago.
A complete theory course on Probability with the following
broad topics, based on the book by A. Papaulin.

1) Axioms of Probability
2) Random Variable
3) Functions of Random Variable
4) Joint moment and densities
5) Random Sequence

After the theory the professor introduces Random Processes
and applies the theory developed in the class till that
point.

6) Random Processes
7) Stationarity
8) Ergodicity
9) Spectral Analysis

Then he go into some applied statistics which again builds
upon the ideas discussed before

10) Spectral Estimation
11) Mean Square Estimation

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