Multivariable Control: Gaussian Processes and Linear Systems

This lecture covers topics such as conditional and marginal densities, linear stochastic systems, two-dimensional Gaussian density, posterior height density, affine transformation of Gaussian random vectors, and linear systems driven by Gaussian noise. It also discusses the mean and variance of random variables, the convergence of variance, Lyapunov equations, and steady-state processes. Examples include linear transformations, rotations, translations, and stable/unstable systems, with associated Gaussian densities and confidence intervals.