Prerequisites: graduate standing or consent of instructor
Description: A study of probability theory and stochastic processes, with a focus on the most fundamental aspect of modern communication, con- trol, and signal processing systems driven by random signal inputs. Topics include random variables and stochastic processes; spectral analysis; Wiener opti- mum filter, matched filter, and Karhunen-Loeve expansion; mean square estimation theory including smoothing, filtering, and linear prediction; Levinson’s algorithm, lattice filters, and Kalman filters; and the Markov process.