Detection, Estimation and Learning Theory

Catalog description

Topics include hypothesis testing: Bayes, minimax and Neaman-Pearson detection, asymptotic relative efficiency; Karhunen-Loeve representation; optimal discrete-, continuous-time and sequential detection: structure and performance; maximum likelihood, minimum mean square-error, and maximum a posteriori estimation; sufficient statistics; generalized bounds on estimator performance; linear estimation; Kalman-Bucy and extended Kalman filtering; importance sampling; blind estimation; and elements of compressive sensing.