Title :
On resolution and exponential discrimination between Gaussian stationary vector processes and dynamic models
Author :
Kazakos, Dimitri
Author_Institution :
University of Massachusetts, Amherst, MA, USA
fDate :
4/1/1980 12:00:00 AM
Abstract :
We derive new necessary and sufficient conditions for exponentially convergent discrimination between two stationary vector Gaussian processes, and relate them to previously studied conditions for parameter identifiability and consistent discrimination.
Keywords :
Decision procedures; Gaussian processes; Linear systems, stochastic discrete-time; Parameter identification; Bayesian methods; Convergence; Covariance matrix; Error correction; Fasteners; Gaussian processes; Probability density function; Sufficient conditions;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1980.1102275
