Stochastic Processes, Detection, and Estimation
6.432 · Electrical Engineering and Computer Science · Graduate · Spring 2004
Prof. Gregory Wornell, Prof. Alan Willsky
This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping and whitening filters, and Karhunen-Loeve expansions; and detection and estimation from waveform obs…
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Prof. Gregory Wornell, Prof. Alan Willsky. 6.432 Stochastic Processes, Detection, and Estimation. Spring 2004. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: CC BY-NC-SA 4.0.
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