Material Detail
6.432 Stochastic Processes, Detection, and Estimation | Electrical Engineering and Computer Science
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:
- May 12, 2016
- Date Modified in MERLOT:
- May 12, 2016
- Author:
- Unknown (Know the author?)
- Submitter:
-
Barbra Bied Sperling
- Primary Audience:
- Graduate School
- Technical Format:
- Website
- Mobile Compatibility:
- Not specified at this time
- Language:
- English
- Cost Involved:
- No
- Source Code Available:
- No
- Accessibility Information Available:
- Unknown
- Creative Commons:
-
This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States
