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Stochastic Processes, Detection, and Estimation

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Stochastic Processes, Detection, and Estimation

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 observations. Advanced topics include: linear prediction and spectral estimation, and Wiener and Kalman filters.

Tags: Before 1300: Ancient and Medieval HistoryInforInformation environmentsInformation TheoryNutrition
Details: http://ocw.mit.edu/courses/e...n-and-estimation-spring-2004
Categories: Computer Sciences
Starts : 2004-02-01

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