EE 503 : Lecture 29 (Fall 2020, METU)

EE 503 - Statistical Signal Processing and Modeling
Fall 2020, Middle East Technical University, Ankara, Turkey.
Instructor: Prof. Cagatay Candan
Lecture 29

Lecture Contents:
00:00:00 - Estimation Problem
00:06:15 - Classification of Estimation Problems (non-random, random)
00:11:20 - Maximum likelihood estimation
00:17:13 - Non-random parameter estimation
00:17:24 - Example: x[n] = c + w[n], w[n]: AWGN, c: non-random, Find chat_ML.
00:23:35 - log-likelihood (example, cont'd)
00:30:20 - Properties of Estimators
00:30:35 - Bias (properties of estimators)
00:39:47 - Consistency (properties of estimators)
00:52:03 - Efficiency (properties of estimators)
01:05:20 - Example: CRB for a parameter non-linearly related with observations (illustration)
01:10:01 - Asymptotical efficiency (properties of estimators)
01:12:27 - Folk's Theorem: ML is an asymptotically unbiased and efficient estimator