Stochastic Processes: Spectral Density, Markov Chains, and Gaussian Variables

This lecture covers topics such as spectral density of stochastic processes, properties of white Gaussian noise, Markov chains with absorbing states, characteristic functions of random variables, and independence of Gaussian and exponential variables. It also discusses the transition matrix of a two-state Markov chain, properties of stationary stochastic processes, and the probability generating function of a binomial random variable.