Probability and Random Processes - Study24x7
Probability and Random Processes

Probability and Random Processes

Validity Unlimited Probability and Random Processes All Level Probability and Random Processes English

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Description

In probability theory and related fields, a stochastic orrandom process is a mathematical object usually defined as a family of random variables. ... Furthermore, seemingly random changes in financial markets have motivated the extensive use of stochastic processes in finance.

Includes

  • Probability and Random Processes Total 1 Module
  • Probability and Random Processes 39 Videos
  • Probability and Random Processes Downloadable on mobile
  • Probability and Random Processes Published on 26 June, 2019

Course Content

Probability and Random Processes

Probability and Random Processes
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    Introduction to the Theory of Probability

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    Axioms of Probability

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    Axioms of Probability (Contd.)

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    Introduction to Random Variables

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    Probability Distributions and Density Functions

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    Conditional Distribution and Density Functions

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    Function of a Random Variable

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    Function of a Random Variable (Contd.)

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    Mean and Variance of a Random Variable

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    Moments

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    Characteristic Function

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    Two Random Variables

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    Function of Two Random Variables (Contd.)

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    Correlation Covariance and Related Innver

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    Vector Space of Random Variables

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    Joint Moments

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    Joint Characteristic Functions

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    Joint Conditional Densities

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    Joint Conditional Densities (Contd.)

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    Sequences of Random Variables

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    Sequences of Random Variables (Contd.)

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    Correlation Matrices and their Properties

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    Correlation Matrices and their Properties

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    Conditional Densities of Random Vectors

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    Characteristic Functions and Normality

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    Tchebycheff Inequality and Estimation of an Unknown Parameter

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    Central Limit Theorem

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    Introduction to Stochastic Process

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    Stationary Processes

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    Cyclostationary Processes

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    System with Random Process at Input

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    Ergodic Processes

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    Introduction to Spectral Analysis

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    Spectral Analysis Contd.

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    Spectrum Estimation - Non Parametric Methods

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    Spectrum Estimation - Parametric Methods

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    Autoregressive Modeling and Linear Prediction

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    Linear Mean Square Estimation - Wiener (FIR)

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    Adaptive Filtering - LMS Algorithm

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