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A Generalized Maxentropic Inversion Procedure for Noisy Data

Authors
Publisher
Elsevier B.V.
Publication Date
Keywords
  • Statistics
  • Applied Math Other
  • Probability Theory And Stochastic Processes
  • Statistics/0107010
Disciplines
  • Social Sciences

Abstract

In this note we present a way of solving linear inverse problems with convex constraints and noisy data. Combining the methods of maximum entropy in the mean and the generalized maximum entropy, we are able to treat both the noiseless and the noisy cases as conceptually the same problem. This new generalized inversion procedure is easy to apply and compute and is useful for a large class of models in the natural and social sciences. Three detailed examples are developed and discussed.

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