E1 244, Spring 2016 | Parimal Parag

Estimation and Detection Theory

Lectures

12 Jan 2016: Lecture-01 Bayesian Hypothesis Test
14 Jan 2016: Lecture-02 Minimax Hypothesis Test
19 Jan 2016: Lecture-03 Neyman-Pearson Hypothesis Test
21 Jan 2016: Lecture-04 Example of Simple Binary Hypothesis Test
22 Jan 2016: Lecture-05 Composite Binary Hypothesis Test
28 Jan 2016: Lecture-06 Composite Binary Hypothesis Test, and properties of random samples
02 Feb 2016: Lecture-07 Properties of random samples, and order statistics
04 Feb 2016: Lecture-08 Principles of data reduction
11 Feb 2016: Lecture-09 Sufficient Statistics
12 Feb 2016: Lecture-10 Signal Detection in Discrete Time
16 Feb 2016: Lecture-11 Detection of deterministic signals in Gaussian noise
18 Feb 2016: Lecture-12 Detection of signals with random parameters
23 Feb 2016: Lecture-13 Detection of (purely) stochastic signals in noise
25 Feb 2016: Lecture-14 Detector performance analysis techniques
01 Mar 2016: Lecture-15 Chernoff bounds and sequential detection
03 Mar 2016: Lecture-16 Sequential detection (Contd)
10 Mar 2016: Lecture-17 Point Estimation
15 Mar 2016: Lecture-18 Performance analysis of estimators and Cramer-Rao lower-bound
17 Mar 2016: Lecture-19 Cramer-Rao lower-bound (Contd)
22 Mar 2016: Lecture-20 Best unbiased estimator
24 Mar 2016: Lecture-21 Loss function framework for point estimation
29 Mar 2016: Lecture-22 Maximum likelihood estimation
31 Mar 2016: Lecture-23 Kalman filtering
05 Apr 2016: Lecture-24 Linear MMSE Estimation
07 Apr 2016: Lecture-25 Levinson-Durbin Algorithm
12 Apr 2016: Lecture-26 Expectation-Maximization Algorithm

Homeworks

Homework 1
Homework 2
Homework 3
Homework 4

Tests

Course Syllabus

TBA.

Course Description

This course is designed to enable engineering graduate students to learn basic estimation and detection theory applied to communications and signal processing.

GitHub Information

All the students in the class have access to Estimation-Detection course public repository on GitHub.
Please follow the guidelines in the sample lecture.
The source file for the sample lecture is in the repository.
It is recommended you save it with another name in your local repository for creating a new lecture.
A good book for Git is here and a simple tutorial here.