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Course Rationale and Goals
Classical detection and estimation theory (binary hypothesis testing, M-ary hypothesis testing, composite hypothesis, Bayes estimation, nonrandom parameter estimation, performance evaluation). Representation of random processes. Detection of signals - estimation of signal parameters.
Catalog Course Description
Decision theory:
Binary hypothesis testing, M-ary testing, Bayes, Neyman-Pearson, Min-Max.
Performance. Probability of error, ROC. Estimation theory: linear and nonlinear
estimation, parameter estimation. Bayes, MAP, maximum likelihood,
Cramér-Rao bounds. Bias, efficiency, consistency. Asymptotic properties
of estimators. Orthogonal decomposition of random processes and harmonic
representation. Waveform detection and estimation. Wiener filtering and
Kalman-Bucy filtering. Recursive algorithms. Spectral estimation. (Recommended: EE 571)