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Stochastic Processes, Detection, and Estimation, Spring 2004

(Complete Item Description)

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.

Subject:: Science and Technology
Grade Level:: Post-secondary
Collection:: MIT OpenCourseWare

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Top Keywords

Bayesian Communications Computer Sciences control Cramer-Rao Bounds Detection Electrical Engineering Estimation Kalman Filter Karhunen-Loeve Expansion Linear Prediction Minimum-Variance Unbiased Estimator Neyman-Pearson Shaping Filter Signal Processing Spectral Estimation Stochastic Process Vector Spaces Waveform Observation Whitening Filter Wiener Filter

Conditions of Use

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Read the Fine Print

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Your redistributing comes with some restrictions. Do not remix or make derivative works.

Copyrighted materials, available under Fair Use and the TEACH Act for US-based educators, or other custom arrangements. Go to the resource provider to see their individual restrictions.

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