Title : Estimation Theory
Course No : EE5111
Credits : 4
Prerequisite :

Syllabus :

Introduction: The mathematical estimation problem

  • Minimum Variance Unbiased Estimators: Unbiased estimators, Minimum variance criterion, Cramer-Rao lower bound, Efficient estimators, Sufficient statistics, Rao-Blackwell Theorem, Vector parameter estimation, Estimators for Gaussian models
  • Practical Estimators: BLUE, Maximum likelihood estimators, Asymptotic properties of MLE, Least squares, Linear and Nonlinear techniques, Method of moments
  • Bayesian Estimators: Risk functions, MMSE estimators, MAP estimators, Linear Bayesian estimators, Vector parameter estimation, Sequential estimators
  • Additional Topics: Wiener filters, Kalman filters, Extensions for complex data and parameters

 Text Books :

  • Fundamentals of Statistical Signal Processing: Estimation Theory, Volume I, Steven M Kay, Pearson, 2010.

References :

  • An Introduction to Signal Detection and Estimation, H Vincent Poor, Springer,1998.
  • Detection, Estimation and Modulation Theory: Part I, Harry L Van Trees and Kristine L Bell, Wiley-Blackwell, second edition, 2013.