• DocumentCode
    701936
  • Title

    Adaptive encoding and prediction of hidden Markov processes

  • Author

    Gerencser, L. ; Molnar-Saska, G.

  • Author_Institution
    MTA SZTAKI, Computer and Automation Institute, Hungarian Academy of Sciences, 13-17 Kende u., Budapest 1111, Hungary
  • fYear
    2003
  • fDate
    1-4 Sept. 2003
  • Firstpage
    791
  • Lastpage
    795
  • Abstract
    The purpose of this paper is to provide explicit results on the almost sure asymptotic performance of adaptive encoding and prediction procedures for finite-state Hidden Markov Models. In addition, Rissanen´s tail condition [14] will be verified, from which a lower bound for the mean-performance of universal encoding procedures will be derived. The results of this paper are based on [10].
  • Keywords
    Complexity theory; Encoding; Hidden Markov models; Markov processes; Maximum likelihood estimation; Stochastic systems; Hidden Markov Models; adaptive encoding; adaptive prediction; maximum-likelihood estimation; stochastic complexity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    European Control Conference (ECC), 2003
  • Conference_Location
    Cambridge, UK
  • Print_ISBN
    978-3-9524173-7-9
  • Type

    conf

  • Filename
    7085054
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=701936