• DocumentCode
    1303402
  • Title

    The marginal likelihood for parameters in a discrete Gauss-Markov process

  • Author

    Bell, Bradley M.

  • Author_Institution
    Dept. of Bioeng., Washington Univ., Seattle, WA, USA
  • Volume
    48
  • Issue
    3
  • fYear
    2000
  • fDate
    3/1/2000 12:00:00 AM
  • Firstpage
    870
  • Lastpage
    873
  • Abstract
    We use Laplace´s method to approximate the marginal likelihood for parameters in a Gauss-Markov process. This approximation requires the determinant of a matrix whose dimensions are equal to the number of state variables times the number of time points. We reduce this to sequential evaluation of determinants and inverses of smaller matrices, we show this is a numerically stable method
  • Keywords
    Gaussian processes; Markov processes; adaptive Kalman filters; determinants; matrix inversion; Laplace method; approximation; determinant; dimensions; discrete Gauss-Markov process; inverses; marginal likelihood; matrix; numerically stable method; parameters; sequential evaluation; state variables; time points; Adaptive filters; Approximation algorithms; Filtering; Gaussian approximation; Gaussian processes; Noise measurement; Parameter estimation; State estimation; Symmetric matrices; Time measurement;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.824682
  • Filename
    824682
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1303402