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Maximum Entropy Reconstruction Using Derivative Information Part 1: Fisher Information and Convex Duality

Borwein, Jonathan M. and Lewis, Adrian and Noll, Dominik (1996) Maximum Entropy Reconstruction Using Derivative Information Part 1: Fisher Information and Convex Duality. Mathematics of Operations Research, 21 (2). pp. 442-468.

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      Abstract

      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 variety 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 information 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.

      Item Type: Article
      Additional Information: pubdom FALSE
      Uncontrolled Keywords: partially finite convex programming, duality, Fisher information, generalized solutions, maximum entropy method, optimal control, spectral density estimation
      Subjects: UNSPECIFIED
      Faculty: UNSPECIFIED
      Depositing User: Users 1 not found.
      Date Deposited: 17 Nov 2003
      Last Modified: 07 Sep 2014 21:18
      URI: https://docserver.carma.newcastle.edu.au/id/eprint/82

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