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Random Vectors: Stochastic Models for Communications
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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