Stochastic Processes, Detection, and Estimation's Full Profile
This courseexamines 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, andKarhunen-Loeve expansions; anddetection and estimation from waveform observations. Advanced topics include: linear prediction and spectral estimation, andWiener and Kalman filters.
Feb 01, 2004
to May 25, 2004
Days of the Week:
Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
- Level of Difficulty: Beginner
- Size: Massive Open Online Course
- Instructors: Prof. Alan Willsky, Prof. Gregory Wornell
- Cost: Free
- Institution: MIT OCW
- Topics: Signal Processing
About MIT OCW:
MIT OpenCourseWare (OCW) is a web-based publication of virtually all MIT course content. OCW is open and available to the world and is a permanent MIT activity.
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MIT OpenCourseWare (MIT OCW) is an initiative of the Massachusetts Institute of Technology (MIT) to put all of the educational materials from its undergraduate- and graduate-level courses online, partly free and openly available to anyone, anywhere.