Covers the stochastic properties of time series, stationarity, autocovariance, special stochastic processes, spectral density, digital filters, estimation techniques, model checking, forecasting, and advanced models.
Discusses the fundamentals of probability and stochastic processes, focusing on random variables, their properties, and applications in statistical signal processing.
Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.
