Maximum entropy reconstruction using derivative information, part I: Fisher information and convex duality

Title: Maximum entropy reconstruction using derivative information, part I: Fisher information and convex duality
Creator: Borwein, J. M.; Lewis, A. S.; Noll, D.
Relation: Mathematics of Operations Research Vol. 21, Issue 2, p. 442-468
Publisher Link: http://dx.doi.org/10.1287/moor.21.2.442
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Resource Type: journal article
Date: 1996
Description: Maximum entropy spectral density estimation is a technique for reconstructing an unknown density functiOn from some known measurements by maximizing a given measure of entropy of the estimate. Here we present a vanety of new entropy measures which attempt to control derivative values of the densities. Our models apply among others to the inference problem based on the averaged Fisher mformation measure. The duality theory we develop resembles models used in convex optimal control problems. We present a variety of examples, including relaxed moment matching with Fisher information and best interpolation on a strip.
Subject: partially finite convex programming; duality; Fisher information; generalized solutions; maximum entropy method; optimal control; spectral density estimation
Identifier: http://hdl.handle.net/1959.13/940888
Identifier: uon:13124
Identifier: ISSN:0364-765X
Language: eng
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