Estimation and Linear Prediction - Part 2

This lecture covers the estimation and prediction of random signals, focusing on power spectral density and the Wiener-Khintchine theorem. It explains the laborious process of calculating the power spectral density for stationary random signals and the properties of discrete-time random signals. The concept of ergodicity is introduced, discussing how statistical averages can be estimated from a single realization. The lecture also delves into the convergence of statistical estimates and the ergodicity of the mean and correlation. Different types of signals, including stationary, non-stationary, and ergodic processes, are explored in the context of signal processing.