Stochastic Processes: Second Order Analysis

Stochastic Processes: 2nd Order Analysis

Explores stochastic processes, stationarity, ergodicity, and Wiener filtering for image restoration.

Spectral Density and Cross Power Signals

Covers spectral density, cross power signals, stationarity, and Gaussian processes.

Covers stochastic models for communications, including stationarity, ergodicity, power spectral density, and Wiener filter.

Continuous-Time Stochastic Processes: Ergodicity Examples

Covers examples of ergodicity in continuous-time stochastic processes, illustrating concepts such as ergodicity and random processes.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.