EE 527

EE 527
Detection and Estimation Theory

Spring 2007
TR 9:30-10:50, Howe 1242
Distance education information

Instructor: Aleksandar Dogandzic
- Room: 3119 Coover
- E-mail: ald(at)iastate.edu
- Office hours: MW 2-3
Text: S.M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Prentice Hall, 1993, pt. I.
Reference books:
- S.M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory. Prentice Hall, 1998, pt. II.
- P.J. Bickel and K.A. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics. Prentice-Hall, 2001.
- A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin, Bayesian Data Analysis. Chapman & Hall, 2004.
- L. Wasserman, All of Statistics: A Concise Course in Statistical Inference. Springer-Verlag, 2004.
- T.K. Moon and W.C. Stirling, Mathematical Methods and Algorithms for Signal Processing, Prentice-Hall, 2000.
- H.V. Poor, An Introduction to Signal Detection and Estimation, 2nd ed. Springer-Verlag, 1994.
- J.S. Liu, Monte Carlo Strategies in Scientific Computing. Springer-Verlag, 2001.
- B.D. Ripley, Stochastic Simulation. Wiley, 1987.
Course Outline:
- Estimation Theory
  - Background material,
  - Cramér-Rao bound (CRB),
  - Minimum variance unbiased estimation (MVUE), best linear unbiased estimation (BLUE),
  - Maximum likelihood estimation (MLE)
  - Bayesian inference, sequential Bayesian approach and Kalman filter,
    Bayesian EM algorithm, iterative conditional modes,
  - Monte Carlo (MC) and Markov chain Monte Carlo (MCMC) methods, particle filters,
  - Hidden Markov models, forward and backward recursions, Viterbi algorithm,
  - Introduction to graphical models, Markov graphs, inference on graphs using belief propagation,
  - Signal-processing, NDE, and communications applications.
- Detection Theory
- Grading: (tentative)
  - 50% Homework and projects,
  - 25% Midterm examination,
  - 25% Final examination.
- Midterm Exam: March 8.

Handouts
Homework:
- HW #6
- HW #7
- HW #8
- Examples
Interesting papers
Acknowledgment

Useful links