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-2011~~09-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