The University of Texas at Austin
Department of Electrical and Computer
EE381K-6 Estimation Theory
Fall Semester 2017
Instructor: Prof. Haris Vikalo
- Email: hvikalo AT ece DOT utexas DOT edu
- Phone: (512) 232-7922
- Office hours: EER 7.810; Tue, Thu 3:30pm-4:30pm
Teaching Assistant: Abolfazl Hashemi
Lectures: ECJ 1.314, Tue, Thu 2:00pm-3:30pm
- Email: firstname.lastname@example.org
- Office hours: TBA
T. Kailath, A. Sayed, and B. Hassibi, Linear Estimation, Prentice Hall, 2000.
S. M. Kay, Fundamentals of Statistical Signal Processing, Vol. I: Estimation Theory, Prentice-Hall, 1993.
B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter, Artech House, 2004.
- Additional material: Links and references.
- Homeworks: 20%
- Midterm exam: 40%
- Final project: 40% (follow link for details)
Homework policy: Homeworks are to be submitted at the
beginning of the class when they are due. You may discuss homework problems
with other students, but must submit your own independent solution. Late
homework assignments will not be accepted.
- Prerequisites: Background in random processes (EE 381J or equivalent) is
- Course description:
Introduction to the fundamentals of estimation theory, with
applications to stochastic and adaptive signal processing. Topics include classical and Bayesian parameter estimation;
Cramer-Rao bounds; deterministic and stochastic least-squares estimation; the innovation process;
spectral factorization and Wiener filtering; state-space structure and Kalman
filters; LMS and RLS adaptive filters; Markov chain Monte
Carlo methods; Bayesian and particle filters; expectation-maximization algorithm; non-parametric
- Course outline (tentative):
- Introduction and Foundations
- Parameter estimation: Classical and Bayesian
- Cramer-Rao lower bounds
- Deterministic least-squares problems
- Stochastic least-squares problems
- The innovation process
- Estimation of Stationary Processes
- Innovations for stationary processes
- Wiener theory for scalar processes
- Recursive Wiener filters
- Estimation of Nonstationary Processes
- State-space models
- Kalman filter
- Smoothed estimators
- Continuous-Time Estimation
- Continuous-time state-space estimation
- Bayesian filtering, MCMC methods
- Markov chain Monte Carlo methods
- Particle filters
- Expectation-maximization algorithm
- Non-parametric Bayesian methods
- Dirichlet processes
- Blackwell-MacQueen urn scheme, Chinese restaurant process, stick-breaking construction
- Notice for students with disabilities
Students with disabilities may request appropriate academic accommodations from the Division of Diversity
and Community Engagement, Services for Students with Disabilities, 471-6259, http://www.utexas.edu/diversity/ddce/ssd/.
- Emergency instructions: Classroom evacuation for students
All occupants of university buildings are required to evacuate a building when a fire alarm and/or
an official announcement is made indicating a potentially dangerous situation within the building.
Familiarize yourself with all exit doors of each classroom and building you may occupy. Remember that
the nearest exit door may not be the one you used when entering the building. If you require
assistance in evacuation, inform your instructor in writing during the first week of class
For evacuation in your classroom or building:
- Follow the instructions of faculty and teaching staff.
- Exit in an orderly fashion and assemble outside.
- Do not re-enter a building unless given instructions by emergency personnel.