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Comparison of Minimum Cross-Entropy Inference with Minimally Informative Information Systems

  • N. C. Dalkey
Part of the Fundamental Theories of Physics book series (FTPH, volume 31-32)

Abstract

The Minimum Cross-Entropy (MXE) inference rule leads to information systems which are inconsistent, and which may have an expectation less than the prior. The Min-Score rule (a generalization of maximum entropy) applied to information systems generates consistent systems and has a guaranteed expectation at least as great as the prior. The guaranteed expectation for the Min-Score rule is always at least as great as that for MXE.

Keywords

Prior Probability Maximum Entropy Bayesian Method Inference Rule Inductive Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Dalkey, N. C. (1983). Updating Inductive Inference, presented at the third Workshop on Maximum Entropy and Bayesian Methods in Applied Statistics, University of Wyoming, August 1–4.Google Scholar
  2. Dalkey, N. C. (1985). Inductive Inference and the Maximum Entropy Principle. In Maximum Entropy and Bayesian Methods in Inverse Problems, eds. C. Ray Smith and W. T. Grandy, pp. 351–64. Dordrect/Boston/Lancaster: D. Reidel.Google Scholar
  3. Dalkey, N. C. (1986). Prior Probabilities Revisited. In Maximum Entropy and Bayesian Methods in Applied Statistics, ed. J. H. Justice, pp. 117–30. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  4. Shore, J. E. & Johnson, R. W. (1981) Properties of Cross-Entropy Minimization. IEEE Transactions on Information Theory, IT-27, 472–82.Google Scholar

Copyright information

© Kluwer Academic Publishers 1988

Authors and Affiliations

  • N. C. Dalkey
    • 1
  1. 1.Dept. of Computer ScienceUCLAUSA

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