Title :
A sufficient condition for consistent discrimination between stationary Gaussian models
Author_Institution :
Tel-Aviv University, Tel-Aviv, Israel
fDate :
10/1/1978 12:00:00 AM
Abstract :
The uniqueness of the prediction error covariance matrix is shown to be sufficient for a consistent selection among a finite set of stationary Gaussian models, employing the maximum-likelihood criterion. The new consistency condition is considerably easier to verify than previously suggested conditions.
Keywords :
Gaussian processes; Modeling; Parameter identification; maximum-likelihood (ML) estimation; Adaptive control; Covariance matrix; Mathematical model; Maximum likelihood detection; Medical diagnosis; Pattern recognition; Predictive models; Radar detection; Sufficient conditions; Technological innovation;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1978.1101874