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ESTIMATION AND DECISION THEORY

ECE 830
Course Description

Estimation and decision theory applied to random processes and signals in noise: Bayesian, maximum likelihood, and least squares estimation; the Kalman filter; maximum likelihood and maximum aposteriori detection; adaptive receivers for channels with unknown parameters or dispersive, fading characteristics; the RAKE receiver; detection systems with learning features.

Prerequisites
Satisfies
Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.71

12.55% from Historical

Completion Rate
100%

2.15% from Historical

A Rate
71.43%

105.63% from Historical

Class Size
7

-55.79% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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