• DocumentCode
    1855294
  • Title

    Nonlinear time series prediction weighted by marginal likelihoods: a hierarchical Bayesian approach

  • Author

    Matsumoto, T. ; Saito, M. ; Sugi, J.

  • Author_Institution
    Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan
  • Volume
    4
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    2604
  • Abstract
    A nonlinear time series prediction scheme is proposed with a combination of model dynamical systems weighted by marginal likelihoods. The scheme outperforms prediction with a single model prediction with the highest marginal likelihood
  • Keywords
    Bayes methods; multilayer perceptrons; nonlinear dynamical systems; parameter estimation; time series; hierarchical Bayesian algorithm; marginal likelihood; multilayer perceptron; nonlinear dynamical systems; parameter estimation; time series prediction; Bayesian methods; Distributed computing; Equations; Markov processes; Neural networks; Noise level; Nonlinear dynamical systems; Predictive models; Uncertainty; Yttrium;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.833486
  • Filename
    833486
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1855294