Electronics - Probability & Random Variables (English)

   Wednesday, February 05, 2025   

Starting a new Lecture Notes Series on Electronics - Probability & Random Variables

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Electronics - Probability & Random Variables By Lecture Notes together!

Lecture 1: Lecture - 1 Introduction to the Theory of Probability

Lecture 2: Lecture - 2 Axioms of Probability

Lecture 3: Lecture - 3 Axioms of Probability (Contd.)

Lecture 4: Lecture - 4 Introduction to Random Variables

Lecture 5: Lecture - 5 Probability Distributions and Density Functions

Lecture 6: Lecture - 6 Conditional Distribution and Density Functions

Lecture 7: Lecture - 7 Function of a Random Variable

Lecture 8: Lecture - 8 Function of a Random Variable (Contd.)

Lecture 9: Lecture - 9 Mean and Variance of a Random Variable

Lecture 10: Lecture - 10 Moments

Lecture 11: Lecture - 11 Characteristic Function

Lecture 12: Lecture - 12 Two Random Variables

Lecture 13: Lecture - 13 Function of Two Random Variables

Lecture 14: Lecture - 14 Function of Two Random Variables (Contd.)

Lecture 15: Lecture - 15 Correlation Covariance and Related Innver

Lecture 16: Lecture - 16 Vector Space of Random Variables

Lecture 17: Lecture - 17 Joint Moments

Lecture 18: Lecture - 18 Joint Characteristic Functions

Lecture 19: Lecture - 19 Joint Conditional Densities

Lecture 20: Lecture - 20 Joint Conditional Densities (Contd.)

Lecture 21: Lecture - 21 Sequences of Random Variables

Lecture 22: Lecture - 22 Sequences of Random Variables (Contd.)

Lecture 23: Lecture - 23 Correlation Matrices and their Properties

Lecture 24: Lecture - 24 Correlation Matrices and their Properties

Lecture 25: Lecture - 25 Conditional Densities of Random Vectors

Lecture 26: Lecture - 26 Characteristic Functions and Normality

Lecture 27: Lecture - 27 Thebycheff Inquality and Estimation

Lecture 28: Lecture - 28 Central Limit Theorem

Lecture 29: Lecture - 29 Introduction to Stochastic Process

Lecture 30: Lecture - 30 Stationary Processes

Lecture 31: Lecture - 31 Cyclostationary Processes

Lecture 32: Lecture - 32 System with Random Process at Input

Lecture 33: Lecture - 33 Ergodic Processes

Lecture 34: Lecture - 34 Introduction to Spectral Analysis

Lecture 35: Lecture - 35 Spectral Analysis Contd.

Lecture 36: Lecture - 36 Spectrum Estimation - Non Parametric Methods

Lecture 37: Lecture - 37 Spectrum Estimation - Parametric Methods

Lecture 38: Lecture - 38 Autoregressive Modeling and Linear Prediction

Lecture 39: Lecture - 39 Linear Mean Square Estimation - Wiener (FIR)

Lecture 40: Lecture - 40 Adaptive Filtering - LMS Algorithm