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.

### 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.

## N.A

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

No Reviews found for this course.

10 STUDENTS ENROLLED

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

help@freevideolectures.com