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6.432 Stochastic Processes, Detection, and Estimation, Spring 2004

Graph showing threshold phenomenon in nonlinear estimation.
Example of threshold phenomenon in nonlinear estimation. (Image courtesy of Alan Willsky and Gregory Wornell.)

Highlights of this Course

This course site features homework assignments and recitation notes.

Course Description

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.


Prof. Alan Willsky
Prof. Gregory Wornell

Course Meeting Times

Two sessions / week
1.5 hours / session

One session / week
1 hour / session




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