• DocumentCode
    2403854
  • Title

    Optimal filtering, prediction and smoothing of hidden Markov models

  • Author

    Li, Zbeng ; Evans, R.J.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
  • fYear
    1992
  • fDate
    1992
  • Firstpage
    3299
  • Abstract
    Optimal filtering, prediction and smoothing algorithms for hidden Markov models (HMMs) are presented. Employing a dynamic state space description of the probability evolution for an HMM, the close structural similarity with Kalman filtering, prediction and smoothing is shown. The HMM estimation algorithms are simpler than those of the Kalman filter
  • Keywords
    Kalman filters; filtering and prediction theory; hidden Markov models; probability; HMM; Kalman filtering; dynamic state space description; hidden Markov models; optimal filtering; optimal prediction; optimal smoothing; probability evolution; Automatic control; Filtering algorithms; Frequency estimation; Hidden Markov models; Kalman filters; Prediction algorithms; Smoothing methods; Speech recognition; State-space methods; Viterbi algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
  • Conference_Location
    Tucson, AZ
  • Print_ISBN
    0-7803-0872-7
  • Type

    conf

  • DOI
    10.1109/CDC.1992.371027
  • Filename
    371027
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2403854