Explores Gaussian vectors, moment generating functions, independence, density functions, affine transformations, and quadratic forms.
Covers the basics of linear algebra, including matrix operations and singular value decomposition.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Delves into the proof of the convergence rate theorem for an ergodic Markov chain, emphasizing eigenvalues and detailed balance properties.
