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Syllabus

Course Number 0510-6202-01
Course Name Estimation Theory
Academic Unit The Iby and Aladar Fleischman Faculty of Engineering -
School of Electrical Engineering
Lecturer Prof. Ofer ShayevitzContact
Contact Email: ofersha@tauex.tau.ac.il
Office Hours By appointmentBuilding: Wolfson - Electrical Eng.
Mode of Instruction Lecture
Credit Hours 3
Semester 2021/2
Day Thu
Hours 16:00-19:00
Building Engineering Classrooms
Room 207
Course is taught in English
Syllabus Not Found

Short Course Description

Overview of the parameter estimation problem. Estimation in parametric families. Minimum variance unbiased estimation. Fisher information and the Cramer-Rao lower bound. Linear models and least-squares estimation. The complete sufficient statistics approach. Exponential families. Maximum likelihood estimation and its asymptotic properties. Bayesian estimation: MMSE estimation, Maximum A-Posteriori (MAP) estimation, conjugate priors. Regularization: connection to Bayesian theory, sparse models. Hypothesis testing (detection theory): Bayesian setting, Neyman-Pearson setting, sample complexity, composite hypothesis testing and GLRT. Minimax estimation: Bayesian lower bounds, Le Cam's method. Algorithms: sequential estimation, alternating-minimization, coordinate descent.



Full syllabus is to be published
Course Requirements

Final Exam

Students may be required to submit additional assignments
Full requirements as stated in full syllabus

PrerequisiteRandom Signals and Noise (05123632)

The specific prerequisites of the course,
according to the study program, appears on the program page of the handbook



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