• DocumentCode
    3818218
  • Title

    Minimum entropy of error estimation for discrete random variables

  • Author

    M. Janzura;T. Koski;A. Otahal

  • Author_Institution
    Inst. of Inf. Theory & Autom., Czechoslovak Acad. of Sci., Prague, Czech Republic
  • Volume
    42
  • Issue
    4
  • fYear
    1996
  • Firstpage
    1193
  • Lastpage
    1201
  • Abstract
    The principle of minimum entropy of error estimation (MEEE) is formulated for discrete random variables. In the case when the random variable to be estimated is binary, we show that the MEEE is given by a Neyman-Pearson-type strictly monotonous test. In addition, the asymptotic behavior of the error probabilities is proved to be equivalent to that of the Bayesian test.
  • Keywords
    "Entropy","Error analysis","Random variables","Testing","Bayesian methods","State estimation","Estimation error","Error probability","Predictive coding"
  • Journal_Title
    IEEE Transactions on Information Theory
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/18.508841
  • Filename
    508841
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3818218