Stochastic Models for Communications

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.