EE 527
Detection and Estimation Theory
Spring 2011
T R 2:10-3:30, 1428 Molecular Biology

• Instructor: Aleksandar Dogandzic
  • Room: 3119 Coover
  • Office hours: M W 1-2
  • Email:
• Textbook: 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.
  • H. V. Poor, An Introduction to Signal Detection and Estimation, 2nd ed. Springer, 1994.
  • A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian Data Analysis, 2nd ed. Chapman & Hall, 2004.
  • L. Wasserman, All of Statistics: A Concise Course in Statistical Inference. Springer 2004.
• Course Outline:
  • Estimation Theory
    • Background material,
    • Cramér-Rao bound (CRB),
    • Minimum variance unbiased estimation (MVUE), best linear unbiased estimation (BLUE),
    • Maximum likelihood estimation (MLE)
    • expectation-maximization (EM) algorithm,
    • Newton-Raphson and Fisher scoring algorithms,
    • Bayesian inference, sequential Bayesian approach and Kalman filter,
      Bayesian EM algorithm,
    • An introduction to Monte Carlo (MC) and Markov chain Monte Carlo (MCMC) methods, particle filters,
    • Hidden Markov models, forward and backward recursions, Viterbi algorithm,
    • An introduction to probabilistic graphical models,
    • Signal-processing, NDE, and communications applications.
  • Detection Theory
  • Background material,
  • Bayes detectors,
  • Neyman-Pearson detectors (matched filter, estimator-correlator, etc),
  • Multiple hypothesis tests,
  • Signal-processing, NDE, and communications applications.
• Grading: (tentative)
  • 30% Homework and projects,
  • 40% Midterm examinations,
  • 30% Final examination.
• Handouts
  • #0 #0b #1 #2 #3 #4 Review #1 #4b #5 #6 Review #2 Graphical models
  #7 #8 #9 #10 #11 #12
  • On biased estimators and the unbiased Cramér-Rao lower bound by P. Stoica and R. L. Moses