EE 236: State and Parameter Estimation Theory

← 2021-22 2022-23 2023-24 →
← 2018-19 ← 2018-19 changes (lax) changes (strict) ▾

Units: 4

Hours: Lecture, 3 hours; discussion, 1 hour

Catalog page 335

Prerequisites: EE 215 with a grade of C or better; graduate standing

Description: Covers Fisher information, Cramer-Rao lower bound, efficiency, and sufficient statistics. Addresses minimum variance unbiased, best linear unbiased, maximum likelihood, least squares, maximum a posteriori, and mean-squared estimation. Also covers Weiner and Kalman filtering as well as applications in navigation, signal processing, machine learning, and dynamical systems.

Cross-listing: Cross-listed with ME 236.

Derived Information — The following is not part of the official catalog but is computed from catalog data.

Serves as a prerequisite for

CS 228 EE 228 EE 245 ME 222
Prerequisite graph not available.

Referenced in

Enrollment History (from UCR Banner, not catalog)
Combined: EE 236 / ME 236
Year F W S Su Total
2025-26  8/45  8/45
2024-25  9/45  9/45
2023-24 7/30  7/30
2022-23 15/45 15/45
2021-22 19/45 19/45
2020-21 22/45 22/45
2019-20 21/45 21/45
2018-19 32/39 32/39
2017-18 25/39 25/39
2016-17 17/31 17/31