Introduction, Axioms of Probability, Random Variables,Probability Distributions and Density Functions, Function of a Random Variable, Mean and Variance, Function of Two Random Variables, Correlation Covariance, Correlation Matrices and their Properties, Thebycheff Inquality and Estimation, Central Limit Theorem, Ergodic Processes, Linear Mean Square Estimation – Wiener,Adaptive Filtering – LMS Algorithm.

Other Resources

Course Curriculum

Introduction Details 59:50
Axioms of Probability – I Details 59:49
Axioms of Probability – II Details 59:51
Introduction to Random Variables Details 59:37
Probability Distributions and Density Functions Details 59:48
Conditional Distributions and Density Functions Details 59:55
Function of a Random Variable Details 59:53
Function of a Random Variable (Cont .) Details 59:50
Mean and Variance of a Random Variable Details 59:54
Moments Details 59:47
Characteristic Function Details 59:55
Two Random Variables Details 59:53
Function of Two Random Variables Details 1:2:50
Function of Two Random Variables (Cont.) Details 59:47
Correlation Covariance and Related Innver Details 59:54
Vector Space of Random Variables Details 59:47
Joint Moments Details 59:54
Joint Characteristic Functions Details 59:48
Joint Conditional Densities – I Details 59:49
Joint Conditional Densities – II Details 59:57
Sequences of Random Variables – I Details 59:57
Sequences of Random Variables – II Details 59:45
Correlation Matrices and their Properties Details 59:53
Correlation Matrices and their Properties Details 59:50
Conditional Densities of Random Vectors Details 59:56
Characteristic Functions and Normality Details 1:58
Thebycheff Inquality and Estimation Details 59:55
Central Limit Theorem Details 59:55
Introduction to Stochastic Process Details 59:56
Stationary Processes Details 59:49
Cyclostationary Processes Details 59:52
System with Random Process as Input Details 1:1
Ergodic Processes Details 59:57
Introduction to Spectral Analysis Details 59:51
Spectral Analysis Contd. Details 59:57
Spectrum Estimation – Non Parametric Methods Details 59:50
Spectrum Estimation – Parametric Methods Details 1:4
Autoregressive Modeling and Linear Prediction Details 59:50
Linear Mean Square Estimation – Wiener (FIR) Details 59:57
Adaptive Filtering – LMS Algorithm Details 59:47

These video tutorials are delivered by IIT Kharagpur as part of NPTEL online courses program.

Course Reviews

N.A

ratings
  • 5 stars0
  • 4 stars0
  • 3 stars0
  • 2 stars0
  • 1 stars0

No Reviews found for this course.

About

FreeVideoLectures Provides you complete information about best courses online, Video tutorials, helps you in building a career !!

help@freevideolectures.com

Learn More About us

About Us
Privacy Policy
FAQ

FREEVIDEOLECTURES.COM ALL RIGHTS RESERVED.
top
FreeVideoLectures.com All rights reserved.

Setup Menus in Admin Panel