Explores stationarity in stochastic processes, showcasing how statistical characteristics remain constant over time and the implications on random variables and Fourier transforms.
Explores joint distributions, marginal laws, covariance, correlation, and variance properties.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.
