List Estimation, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor) ; Aminzadeh Gohari, Amin (Co-Advisor)
Abstract
Let X be an unknown vector of size n which is to be estimated from a known m 1 vector Y. According to the MMSE criterion, the best estimator (denoted bX(Y)) is an estimator which minimizes the mean squared error. Now, consider a List Decodingproblem in which the sender delivers a list of codes instead of a single decoder. Assume that it is allowed to use multiple parallel estimators (bX1 (Y); ^X2(Y); : : : ; bX k(Y)) instead of delivering a single estimation of samples. The goal is to find the best possible list of estimators, in a way that the mean squared error is optimized between the multiple bX i(Y); (i = 1; 2; : : : ; k). As a medical example, imagine a MRI device which produces three...
Cataloging briefList Estimation, M.Sc. Thesis Sharif University of Technology ; Amini, Arash (Supervisor) ; Aminzadeh Gohari, Amin (Co-Advisor)
Abstract
Let X be an unknown vector of size n which is to be estimated from a known m 1 vector Y. According to the MMSE criterion, the best estimator (denoted bX(Y)) is an estimator which minimizes the mean squared error. Now, consider a List Decodingproblem in which the sender delivers a list of codes instead of a single decoder. Assume that it is allowed to use multiple parallel estimators (bX1 (Y); ^X2(Y); : : : ; bX k(Y)) instead of delivering a single estimation of samples. The goal is to find the best possible list of estimators, in a way that the mean squared error is optimized between the multiple bX i(Y); (i = 1; 2; : : : ; k). As a medical example, imagine a MRI device which produces three...
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