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ECSE 510 Filtering and Prediction for Stochastic Systems (3 credits)

Offered by: Electrical & Computer Engr (Faculty of Engineering)

Overview

Electrical Engineering : Basic notions. Linear state space (SS) systems. Least squares estimation and prediction: conditional expectations; Orthogonal Projection Theorem. Kalman filtering; innovations; Riccati equation. ARMA and SS systems. Stationary processes; Wold decomposition; spectral factorization; Weiner filtering. The Weiner process; linear stochastic differential equations; continuous time filtering. Chapman-Kolmogorov, Fokker-Plank equations. Applications.

Terms: This course is not scheduled for the 2012-2013 academic year.

Instructors: There are no professors associated with this course for the 2012-2013 academic year.

  • (3-0-6)
  • Prerequisites: ECSE 500 and ECSE 509 or equivalent.