Noncausal Gauss Markov random fields: parameter structure and estimation

Author

Balram, Nikhil ; Moura, Jose M F

Author_Institution

IBM, Boca Raton, FL, USA

Volume

39

Issue

4

fYear

1993

fDate

7/1/1993 12:00:00 AM

Firstpage

1333

Lastpage

1355

Abstract

The parameter structure of noncausal homogeneous Gauss Markov random fields (GMRF) defined on finite lattices is studied. For first-order (nearest neighbor) and a special class of second-order fields, a complete characterization of the parameter space and a fast implementation of the maximum likelihood estimator of the field parameters are provided. For general higher order fields, tight bounds for the parameter space are presented and an efficient procedure for ML estimation is described. Experimental results illustrate the application of the approach presented and the viability of the present method in fitting noncausal models to 2-D data

Keywords

Markov processes; information theory; lattice theory and statistics; maximum likelihood estimation; parameter estimation; random processes; signal processing; 2-D data; MLE; finite lattices; first order fields; higher order fields; maximum likelihood estimator; noncausal homogeneous Gauss Markov random fields; parameter estimation; parameter space; parameter structure; second-order fields; tight bounds; Gaussian processes; Image processing; Iterative algorithms; Lattices; Markov random fields; Maximum likelihood estimation; Multidimensional signal processing; Parameter estimation; Partial differential equations; Signal processing algorithms;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.243450

Filename

243450