Stochastic Processes, Detection, and Estimation
Free, no sign-up neededSpring 2004English
About this course
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.
Description from MIT OpenCourseWare.
Where to take this course
- Open MIT OpenCourseWare (new tab)MIT OpenCourseWare · Spring 2004