Spring 2013
T R 23:30, 204 Marston
Instructor
Announcement
Textbooks:

S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Upper Saddle River, NJ: Prentice Hall, 1993.

A. O. Hero, Statistical methods for signal processing, Lecture notes, Ann Arbor, MI, 2008.

D. H. Johnson, Statistical signal processing, Lecture notes, Rice University, Houston, TX, 2013.
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

CramérRao bound and minimum variance unbiased estimation;

Best linear unbiased and leastsquares (LS) estimation;

Maximum likelihood (ML) estimation

computation: expectationmaximization (EM) algorithm;


Bayesian inference

conjugate priors,

Gaussian linear model.

Detection theory

Bayes detectors;

NeymanPearson and classical composite hypothesis tests.
Advanced Topics

Sparse and lowrank 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
Homework