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Lecture# Random Vectors: Stochastic Models for Communications

Description

This lecture covers random vectors, stochastic models for communications, joint cumulative distribution function, joint probability density function, marginal probability density function, independent random variables, conditional probability density function, covariance, joint characteristic function, and complex random variables. It also discusses expectation, covariance properties, joint characteristic function, and multivariate Gaussian random variables.

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Related concepts (143)

COM-300: Stochastic models in communication

L'objectif de ce cours est la maitrise des outils des processus stochastiques utiles pour un ingénieur travaillant dans les domaines des systèmes de communication, de la science des données et de l'i

Teaching assistant

A teaching assistant or teacher's aide (TA) or education assistant (EA) or team teacher (TT) is an individual who assists a teacher with instructional responsibilities. TAs include graduate teaching assistants (GTAs), who are graduate students; undergraduate teaching assistants (UTAs), who are undergraduate students; secondary school TAs, who are either high school students or adults; and elementary school TAs, who are adults (also known as paraprofessional educators or teacher's aides).

Normal distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.

Μ operator

In computability theory, the μ-operator, minimization operator, or unbounded search operator searches for the least natural number with a given property. Adding the μ-operator to the primitive recursive functions makes it possible to define all computable functions. Suppose that R(y, x1, ..., xk) is a fixed (k+1)-ary relation on the natural numbers. The μ-operator "μy", in either the unbounded or bounded form, is a "number theoretic function" defined from the natural numbers to the natural numbers.

Multivariate normal distribution

In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One definition is that a random vector is said to be k-variate normally distributed if every linear combination of its k components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit theorem.

General recursive function

In mathematical logic and computer science, a general recursive function, partial recursive function, or μ-recursive function is a partial function from natural numbers to natural numbers that is "computable" in an intuitive sense – as well as in a formal one. If the function is total, it is also called a total recursive function (sometimes shortened to recursive function). In computability theory, it is shown that the μ-recursive functions are precisely the functions that can be computed by Turing machines (this is one of the theorems that supports the Church–Turing thesis).

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