Schedule (Spring 04)

 

 Topic

Sub-Topic

 Lecture 

Introduction 

1
Vector Observation Non-Bayesian Detection and Estimation Detection Neyman-Pearson Hypothesis Testing 2
Minimax Hypothesis Testing 3
Point Estimation Unformly Minimum Variance Unbiased (UMVU) Estimator 4
Best Linear Unbiased Estimator (BLUE)   5
Maximum Likelihood Estimator (MLE) 6
Expectation Maximization (EM) Algorithm 7

1st Midterm Examination

 Interval Estimation Interval Estimation 8
Bayesian Detection and Estimation Detection General Bayesian Hypothesis Testing 9
Minimax revisited 3
Maximum A Posteriori Probability (MAP) detector 10
Estimation General Bayesian Estimators 11
Maximum A posteriori Probability (MAP) Estimator
Minimum Mean Absolute Error (MMAE) Estimator
Minimum Mean-Squared Error (MMSE) Estimator 12
Linear Minimum Mean-Squared Error (LMMSE) Estimator 13
Affine Minimum Mean-Squared Error (AMMSE) Estimator
Sequence Observation Convergence of a random sequence 14
Non-Bayesian Detection and Estimation Detection Sequential Detection 15
Estimation Sequential Estimation: MLE revisited 16
Bayesian Detection and Estimation Detection Sequential Detection 17
Estimation Estimation of Stationary Process: Wiener Filter 18
Estimation of Nonstationary Process: Kalman Filter 19
Waveform Observation

Karhunen-Loeve (KL) Expansion, Sampling Theorem

  20
Non-Bayesian Detection and Estimation

 

 Detection Correlation Receiver  21
Estimation Classical Estimation revisited  22
Bayesian Detection and Estimation Detection Bayesian Detection revisited  23
Estimation Wiener Filter revisited 24
Kalman Filter revisited  25
Vector, Sequence,  Waveform Observation Estimation Point Estimation Least Squares (LS) 26
Method of moments 27
Spectral Estimation Spectral Estimation

 28