Results - EESC 6343 - UTD TRENDS

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Detection and Estimation Theory

EESC 6343

Erik Jonsson School of Engineering and Computer Science

Parameter estimation. Least-squares, mean-square-error, and minimum-variance estimators. Maximum A Posteriori (MAP) and Maximum-Likelihood (ML) estimators. Bayes estimation. Cramer- Rao lower bound. BLUE estimator and Wiener filtering. Hypothesis testing and data-driven classification algorithms. 3 credit hours.

Prerequisite: EESC 6349.

Offering Frequency: Based on student interest and instructor availability

Grades: 40

Median GPA: A-

Mean GPA: 3.333

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