2019-Spring
EE251 Statistical Detection, Estimation, and Learning

School of Information Science and Technology
ShanghaiTech University

1. Lecturer:
Prof. Xiliang Luo (luoxl@shanghaitech.edu.cn)
Office: SIST 1C-403A, 393 Middle Huaxia Road, Pudong District, Shanghai
Office Hour: Tuesday, Thursday: 11:30am-12:00pm (or just drop by)

TA: Jun Zong (zongjun@shanghaitech.edu.cn)
Office: SIST 1C-402
Office Hour: Monday-Friday afternoon

2. Class Schedule:

Time

Mon

Tue

Wed

Thu

Fri

8:15-9:00am          
9:10-9:55am          
10:15-11:00am   Office Hour, 1C-403A   Office Hour, 1C-403A 1B-110, LEC
11:10-11:55pm   Office Hour, 1C-403A   Office Hour, 1C-403A 1B-110, LEC
1:00-1:45pm          
1:55-2:40pm          
3:00-3:45pm          
3:55-4:40pm          
4:50-5:35pm          
5:45-6:30pm          
6:40-7:25pm          
7:35-8:20pm       1B-110, LEC  
8:30-9:15pm       1B-110, LEC  
9:15-10:00pm          

3. Discussion Group:
To be updated

4. Grading:
* Homework: 30%
* Midterm Exam: 30% [4/12, 10:15-12:15pm, Room 1B-110]
* Final Exam: 40% [6/7, 10:15-12:15pm, Room 1B-110]

5. Textbook:
Fundamentals of Statistical Signal Processing: Estimation Theory/Detection Theory, Steven M. Kay, Prentice Hall, 1993

6. Reference book:
Principles of Signal Detection and Parameter Estimation, Benard C. Levy, Springer, 2008
Detection, Estimation, and Modulation Theory, Part I, Harry L. Van Trees, John Wiley & Sons, Inc., 2001

7. Lecture notes etc.

Week

Lecture Note

HW/Solutions

Additonal Notes

2/18 Introduction, MVUE    
2/25 Cramer-Rao Lower Bound HW1:
Chapter 2: 2.7, 2.11
Chapter 3: 3.6, 3.11, 3.12, 3.13, 3.16, 3.18, 3.19, 3.20
 
3/4 Neyman-Fisher Factorization,
HW2:
Chapter 3: 3.10, 3.14, 3.15, 3.17 + Complete the proof for the asymptotic CRLB
 
3/11 Rao-Blackwell-Lehmann-Scheffe Theorem HW3:
Chapter 5: 5.3, 5.4, 5.5, 5.14, 5.15, 5.16, 5.18
 
3/18 BLUE, MLE HW4:
Chapter 6: 6.1, 6.4, 6.5, 6.9, 6,14
Chapter 7: Appendix 7A: verify the asymptotic optimality of MLE, 7.1, 7.5, 7.6, 7.16, 7.19
 
3/25 MLE, Bayesian Estimator-I HW5:
Chapter 10: 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.10, 10.11
 
4/1

Bayesian Estimator-II , Kalman Filter

HW6:
Chapter 11: 11.7, 11.11, 11.12
Chapter 12: 12.2, 12. 14, 12.19, 12.20
 
4/8

Kalman Filter
Mid-Term Review and In-Class Mid-Term Exam

   
4/15 Kalman Filter HW7:
Chapter 13: 13.4, 13.6, 13.10, 13.11, 13.13, 13.14, 13.15
 
4/22 Introduction to Detetion, ROC HW8:
Levy’s, Chapter 2: 2.4, 2.5, 2.7, 2.8, 2.10
 
4/29 Minimax Test, ROC

HW9:
Levy’s, Chapter 2: 2.9, 2.11, 2.12

 
5/6 M-ary Hypothesis Testing
Asymptotic Performance of LRT
HW10:
Van Trees’s, Chapter 2: 2.2.12, 2.2.15, 2.2.17, 2.2.19
 
5/13 Sequential Tests HW11: Repeated Tests
Read Levy, Chapter 3 and finish the following problems:
3.1, 3.2, 3.3, 3.4, 3.5
 
5/20 Composite Hypothesis Testing HW12: Sequential Tests
Levy, Chapter 3: 3.8, 3.9, 3.10, 3.11
 
5/27 Karhunen-Loeve Decomposition HW13:
Read Levy, Chapter 5 and finish the following problems: 5.2, 5.3, 5.4, 5.5,5.7,5.8, 5.9
 
6/3 Final Exam    
6/10