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Stochastic Processes, Detection, and Estimation

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. Advan...

Start Date: Feb 01, 2004 Topics: Signal Processing
Cost: Free

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Overview

Description

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.

Details

  • Dates: 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

Provider Overview

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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