Welcome to the home page for ECE531 "Principles of Detection and Estimation Theory" for Spring 2011.
announcements
- [08-Apr-2011] A longer article on The Controversy between Fisher and Neyman/Pearson (thanks to Andrew Cavanaugh). You may need to access this article from the WPI domain (or use the WPI proxy).
- [16-Mar-2011] A short article on the main philosophical differences between Bayesian and classical (non-random) parameter estimation (thanks to Andrew Cavanaugh).
- [19-Jan-2011] An email was sent to the class mailing alias ece531@ece.wpi.edu today. If you did not receive this email, please contact Prof. Brown.
general
The required course textbooks are Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory and Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, both by Steven Kay.
The course syllabus (pdf format) including expected course outcomes, grading information, and late policies.
ECE531 academic honesty policies.
ECE531 students with disabilities statement.
lecture notes and handouts
- Lecture 1: Introduction and review essential probabilistic concepts.
- Lecture 2a: A mathematical model for hypothesis testing (finite number of possible observations) and Lecture 2b: Neyman-Pearson hypothesis testing (finite number of possible observations).
- Lecture 3a: A mathematical model for hypothesis testing (infinite number of possible observations), Lecture 3b: Neyman-Pearson hypothesis testing (infinite number of possible observations), and Lecture 3c: Bayesian hypothesis testing.
- Lecture 4a: Detection of Deterministic Discrete-Time Signals and Lecture 4b: Composite hypothesis testing.
- In lecture 5, we will finish the lecture 4b slides and also cover Lecture 5: Detection of Discrete-Time Signals with One or More Unknown Parameters.
- There was no lecture 6 due to the inclement weather cancellation.
- Lecture 7: Bayesian estimation and an introduction to non-random parameter estimation.
- Lecture 8: Non-random parameter estimation. Also, a couple one page handouts on exponential families: Arnold and Lindgren.
- Lecture 9: Information inequality and the Cramer-Rao lower bound.
- Lecture 10a: Best Linear Unbiased Estimation and Lecture 10b: Maximum Likelihood Estimation.
- Lecture 11: Dynamic parameter estimation and the Kalman-Bucy filter.
- Lecture 12: Sequential Detection of Discrete-Time Signals. Also, course evaluations will be distributed in this lecture.
homework and solutions
There will be 10 homework assignments in ECE531, each worth 25 points. The lowest two scores will not be counted.
- Homework 1. Due by 8:50pm on 26-Jan-2011. Solution. Mean=22.3, Median=23, Max=25.
- Homework 2. Due by 8:50pm on
02-Feb-201109-Feb-2011. Solution.Mean=22.9, Median=24.5, Max=25. - Homework 3. Due by 8:50pm on 16-Feb-2011. Solution. Mean=20.0, Median=21.0, Max=25.
- Homework 4. Due by 8:50pm on 23-Feb-2011. Solution.
- Note that there was no homework 5 due to the inclement weather cancellation.
- There is no homework assignment due on 02-Mar-2011.
- Homework 6. Due by 8:50pm on 23-Mar-2011. Solution.
- Homework 7. Due by 8:50pm on 30-Mar-2011. Solution.
- Homework 8. Due by 8:50pm on 06-Apr-2011. Solution.
- Homework 9. Due by 8:50pm on 13-Apr-2011. Solution.
- Homework 10. Due by 8:50pm on 20-Apr-2011. Solution.
examinations and solutions
- A 90 minute midterm exam worth 300 points will be held from 6:00-7:30pm on 02-Mar-2011. Midterm exam and solution.
- A 150 minute comprehensive final exam worth 500 points will be held from 6:00-8:30pm on 27-Apr-2011. Final exam and solution