Material Detail
6.432 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.
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More about this material
- Material Type:
- Online Course
- Date Added to MERLOT:
- August 10, 2012
- Date Modified in MERLOT:
- June 12, 2018
- Authors:
-
Prof. Gregory Wornell
Prof. Alan Willsky - Submitter:
-
Barbra Bied Sperling
- Primary Audience:
- College General Ed, College Lower Division
- Mobile Compatibility:
- Not specified at this time
- Language:
- English
- Cost Involved:
- No
- Source Code Available:
- Unknown
- Creative Commons:
-
This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States
