Stochastic Processes, Detection, and Estimation, Spring 2004

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Author:

Willsky, Alan S.

Subject:

Science and Technology

Institution Name:

M.I.T.

Collection:

MIT OpenCourseWare

Grade Level:

Post-secondary

Abstract:

Fundamentals of detection and estimation for signal processing, communications, and control. 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; Karhunen-Loeve expansions. Detection and estimation from waveform observations. Advanced topics: linear prediction and spectral estimation; Wiener and Kalman filters.

Languages:

English

Material Type:

Activities and Labs, Full Course, Homework and Assignments, Syllabi

Media Format:

Text/HTML, Downloadable docs

Conditions of Use:

Creative Commons Attribution-Noncommercial-Share Alike 3.0