• DocumentCode
    3472971
  • Title

    Model selection in continuous time

  • Author

    Gerencsér, László ; Vágó, Zsuzsanna

  • Author_Institution
    Dept. of Electr. Eng., McGill Univ., Montreal, Que., Canada
  • fYear
    1991
  • fDate
    11-13 Dec 1991
  • Firstpage
    959
  • Abstract
    The foundations of a theory of model selection for continuous-time autoregressive systems is outlined. The authors define the predictive stochastic complexity for continuous-time systems and investigate its asymptotic properties. An almost sure asymptotic result is presented
  • Keywords
    modelling; stochastic processes; stochastic systems; asymptotic properties; continuous-time autoregressive systems; model selection; predictive stochastic complexity; Codes; Continuous time systems; Control systems; Mathematics; Mechanical engineering; Parameter estimation; Recursive estimation; Robot control; Stochastic processes; Stochastic systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 1991., Proceedings of the 30th IEEE Conference on
  • Conference_Location
    Brighton
  • Print_ISBN
    0-7803-0450-0
  • Type

    conf

  • DOI
    10.1109/CDC.1991.261466
  • Filename
    261466
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3472971