Title :
On two-dimensional spectral realization
Author :
Gamboa, F. ; Lavielle, M.
fDate :
9/1/1994 12:00:00 AM
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;
Journal_Title :
Information Theory, IEEE Transactions on