MATH 603 Random Variables and Stochastic Processes
Random variables, vectors, and processes, statistical detection and classification, principles of parameter estimation, biased and unbiased estimators, Cramer-Rao inequality, minimum variance and unbiased estimates, expectation as estimation, Correlation and linear estimation, linear filtering of random processes, discrete time linear models, moving-average and autoregressive processes, discrete time Gauss–Markov process, Maximum likelihood (ML) estimation, Fourier analysis, correlation and coherence, spectral analysis of random signals.
Distribution
3-0-3Offered
Fall Spring