Models for stochastic dependence.
Concepts of description of stationary stochastic processes in the time domain: expectation, covariance, and cross-covariance functions.
Concepts of description of stationary stochastic processes in the frequency domain: power spectrum, cross spectrum.
Special processes: Gaussian process, Wiener process, white noise, Gaussian fields in time and space.
Stochastic processes in linear filters: relationships between in- and out-signals, auto regression and moving average (AR, MA, ARMA), derivation and integration of stochastic processes.
The basics in statistical signal processing: estimation of expectations, covariance function, and spectrum.
Application of linear filters: frequency analysis and optimal filters.