University of Calgary

ENEL 625 - Estimation Theory - Winter 2020

Fundamentals of estimation theory as applied to general statistical signal processing applications such as communication systems, image processing, target and position tracking, and machine learning. Estimator fundamentals including probability density functions, Cramer Rao bounds, Fisher information, linear and nonlinear regression, sufficient statistics, maximum likelihood estimation, minimum mean square error, least squares, Bayesian estimators and concepts. Statistical tracking filters such as Kalman filter and particle filter.
This course may not be repeated for credit.

Hours

  • H(3-0)

Sections

This course will be offered next in Winter 2021.
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