• DocumentCode
    2478494
  • Title

    Asymptotically minimax regret by Bayes mixtures

  • Author

    Takeuchi, Jun-ichi ; Barron, Andrew R.

  • Author_Institution
    C&C Media Res. Lab., NEC, Kanagawa, Japan
  • fYear
    1998
  • fDate
    16-21 Aug 1998
  • Firstpage
    318
  • Abstract
    We study the problem of data compression, gambling and prediction of a sequence xn = x1x2...xn from a certain alphabet X, in terms of regret (Shtarkov 1988) and redundancy with respect to a general exponential family, a general smooth family, and also Markov sources. In particular, we show that variants of Jeffreys mixture asymptotically achieve their minimax values
  • Keywords
    Bayes methods; Markov processes; minimax techniques; prediction theory; redundancy; sequences; Bayes mixtures; Jeffreys mixture; Markov sources; data compression; gambling; general exponential family; general smooth family; prediction; redundancy; sequence; symptotically minimax regret; Data compression; Maximum likelihood estimation; Minimax techniques; National electric code; Statistics; Stochastic processes; Tail; USA Councils;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory, 1998. Proceedings. 1998 IEEE International Symposium on
  • Conference_Location
    Cambridge, MA
  • Print_ISBN
    0-7803-5000-6
  • Type

    conf

  • DOI
    10.1109/ISIT.1998.708923
  • Filename
    708923
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2478494