Spring 2013

T R 2-3:30, 204 Marston

Instructor

Aleksandar Dogandžić
3119 Coover
Office hours: M W 1-2

Announcement

Textbooks:

Reference books:

  • S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory. Upper Saddle River, NJ: Prentice Hall, 1998.

  • H. V. Poor, An Introduction to Signal Detection and Estimation, 2nd. ed., New York: Springer, 1994.

  • A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian Data Analysis, 2nd. ed., New York: Chapman & Hall, 2004.

  • L. Wasserman, All of Statistics: A Concise Course in Statistical Inference. New York: Springer, 2003.

Course outline:

Introduction.

Estimation theory

  • Cramér-Rao bound and minimum variance unbiased estimation;

  • Best linear unbiased and least-squares (LS) estimation;

  • Maximum likelihood (ML) estimation

    • computation: expectation-maximization (EM) algorithm;

  • Bayesian inference

    • conjugate priors,

    • Gaussian linear model.

Detection theory

  • Bayes detectors;

  • Neyman-Pearson and classical composite hypothesis tests.

Advanced Topics

  • Sparse and low-rank signal reconstruction;

  • Introduction to graphical models,

  • Hidden Markov models,

    • Kalman filter,

    • forward and backward recursions, Viterbi algorithm.

Grading: (tentative)

  • 20% Homework assignments,

  • 20% Project,

  • 40% Exams I and II,

  • 20% Final exam.

Handouts

syllabus #1 #2 #3 #4 #5 #6 #7 review I #8 #9 #10 review II #11 #12 #13

Homework

#1 #2 #3 #4 #5 Matlab #6 #7 #8 #9