Prerequisites: EE 215 with a grade of C or better; graduate standing
Description: Covers Fisher information, Cramer-Rao lower bound, efficiency, and sufficient statistics. Addresses minimum variance unbiased, best linear unbiased, maximum likelihood, least squares, maximum a posteriori, and mean-squared estimation. Also covers Weiner and Kalman filtering as well as applications in navigation, signal processing, machine learning, and dynamical systems.
Cross-listing: Cross-listed with EE 236.