• DocumentCode
    1197754
  • Title

    On two-dimensional spectral realization

  • Author

    Gamboa, F. ; Lavielle, M.

  • Volume
    40
  • Issue
    5
  • fYear
    1994
  • fDate
    9/1/1994 12:00:00 AM
  • Firstpage
    1603
  • Lastpage
    1608
  • Abstract
    Reconstruction of a spectral density function from a finite set of covariances can be performed by maximizing an entropy functional. The method of the maximum entropy on the mean is used For computing a discrete version of this spectral density and allows one to give a new interpretation of these reconstruction methods. In fact, the authors show that the choice of the entropy is directly related to a prior distribution. In particular, they consider processes on Z2 . Steepest descent procedures permit the numerical computation of discrete realizations for a wide class of entropies. To ensure the nonnegativity of the solution related to the Burg entropy, they present a new algorithm based on a fixed-point method and the Yule-Walker equations to compute this solution. Then, the solution of the dual problem is obtained as the limit of the trajectory of an ordinary differential equation
  • Keywords
    Bayes methods; Markov processes; digital arithmetic; entropy; spectral analysis; Burg entropy; Yule-Walker equations; a prior distribution; covariances; discrete realizations; discrete version; dual problem; entropy functional; fixed-point method; maximization; maximum entropy; numerical computation; ordinary differential equation; spectral density function; steepest descent procedures; trajectory; two-dimensional spectral realization; Bayesian methods; Density functional theory; Entropy; Equations; Hidden Markov models; Image restoration; Mathematics; Probability distribution; State estimation; Statistical analysis;
  • fLanguage
    English
  • Journal_Title
    Information Theory, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9448
  • Type

    jour

  • DOI
    10.1109/18.333874
  • Filename
    333874