Probability & Random Variables by M. Chakraborty (IIT Kharagpur)

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source: nptelhrd    2008年7月9日
Electronics - Probability & Random Variables by Prof. M. Chakraborty, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur.

Lecture - 1 Introduction to the Theory of Probability 59:50
Lecture - 2 Axioms of Probability 59:49
Lecture - 3 Axioms of Probability (Contd.) 59:51
Lecture - 4 Introduction to Random Variables 59:37
Lecture - 5 Probability Distributions and Density Functions 59:48
Lecture - 6 Conditional Distribution and Density Functions 59:55
Lecture - 7 Function of a Random Variable 59:53
Lecture - 8 Function of a Random Variable (Contd.) 59:50
Lecture - 9 Mean and Variance of a Random Variable 59:54
Lecture - 10 Moments 59:47
Lecture - 11 Characteristic Function 59:55
Lecture - 12 Two Random Variables 59:53
Lecture - 13 Function of Two Random Variables 1:02:50
Lecture - 14 Function of Two Random Variables (Contd.) 59:47
Lecture - 15 Correlation Covariance and Related Innver 59:54
Lecture - 16 Vector Space of Random Variables 59:47
Lecture - 17 Joint Moments 59:54
Lecture - 18 Joint Characteristic Functions 59:48
Lecture - 19 Joint Conditional Densities 59:49
Lecture - 20 Joint Conditional Densities (Contd.) 59:57
Lecture - 21 Sequences of Random Variables 59:57
Lecture - 22 Sequences of Random Variables (Contd.) 59:45
Lecture - 23 Correlation Matrices and their Properties 59:53
Lecture - 24 Correlation Matrices and their Properties 59:50
Lecture - 25 Conditional Densities of Random Vectors 59:56
Lecture - 26 Characteristic Functions and Normality 1:00:58
Lecture - 27 Thebycheff Inquality and Estimation 59:55
Lecture - 28 Central Limit Theorem 59:55
Lecture - 29 Introduction to Stochastic Process 59:56
Lecture - 30 Stationary Processes 59:49
Lecture - 31 Cyclostationary Processes 59:52
Lecture - 32 System with Random Process at Input 1:00:01
Lecture - 33 Ergodic Processes 59:57
Lecture - 34 Introduction to Spectral Analysis 59:51
Lecture - 35 Spectral Analysis Contd. 59:57
Lecture - 36 Spectrum Estimation - Non Parametric Methods 59:50
Lecture - 37 Spectrum Estimation - Parametric Methods 1:00:04
Lecture - 38 Autoregressive Modeling and Linear Prediction 59:50
Lecture - 39 Linear Mean Square Estimation - Wiener (FIR) 59:57
Lecture - 40 Adaptive Filtering - LMS Algorithm 59:47