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40
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Course Description

Probability is the measure of the likeliness that an event will occur. Probability is quantified as a number between 0 and 1 .A random variable, aleatory variable or stochastic variable is a variable whose value is subject to variations due to chance

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