Stochastic Processes, Detection, and Estimation

Graph showing threshold phenomenon in nonlinear estimation.

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

Instructor(s)

MIT Course Number

6.432

As Taught In

Spring 2004

Level

Graduate

Cite This Course

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.

Wornell, Gregory, and Alan Willsky. 6.432 Stochastic Processes, Detection, and Estimation, Spring 2004. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-432-stochastic-processes-detection-and-estimation-spring-2004 (Accessed). License: Creative Commons BY-NC-SA


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