Course Name: EE6130 : Estimation Theory

Description: Introduction Estimation in signal processing- the mathematical estimation problem - assessing estimator performance: Minimum Variance Unbiased Estimation Unbiased estimators-MVU criterionexistence of the MVU-finding the MVU-extension to vector parameter: Cramer-Rao Lower Bound CRLB-Fisher Information-general CRLB for signals in WGN-transformation of parameters-CRLB for vector parameters-transformation of vector parameters-asymptotic CRLB for WSS Gaussian process-signal processing examples: Linear Models: Definition and properties-linear model examples-extension to the linear model: General MVU Estimation Sufficient statistics-finding sufficient statistics by NeymanFisher factorization-using sufficiency to find them VUE-RBLS Theorem-extension to vector parameter: Best Linear Unbiased Estimator Definitionfinding the BLUE-extension to vector parameter-signal processing example: Maximum Likelihood Estimation Motivation-finding the MLE-properties of the MLE-MLE of transformed parameters: the principle of invarianc

Slot: B

RoomNo: ESB207B

Instructor: Sheetal Kalyani

Period: JAN-MAY 2013

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