Stochastic Models for Communications

In course

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Description

This lecture covers random vectors, stochastic models for communications, joint probability density, marginal probability density, independent random variables, functions of two random variables, linear function case, examples of sum of two random variables, expectation, covariance, joint characteristic function, conditional probability density, conditional expectation, complex random variables, Gaussian random variables, and multivariate Gaussian random variables.

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Instructor

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Official source

https://mediaspace.epfl.ch/media/0_37sega30

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Mathematics

Probability theory: Topics in probability theory

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