In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian vector process. A new algorithm is presented for the case in which the process is given in terms of its spectral density function. Contrary to the usual procedure followed, which requires a partial fraction expansion, the algorithm presented starts with a (deterministic) realization of the spectral density function itself
Matrix spectral factorization is traditionally described as finding spectral factors having a fixed ...
The purpose of this paper is to extend the deterministic behavioural theory of J.C. Willems to a sto...
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
A new procedure for stochastic realization of spectral density matrices is briefly reviewed. Afterwa...
A spectral density matrix estimator for stationary stochastic vector processes is studied. As the du...
A spectral density matrix estimator for stationary stochastic vector processes is studied, As the du...
Abstract---We discuss the problem of generating realizations of length N from a Gaussian stationary ...
The subject of modelling and application of stochastic processes is too vast to be exhausted in a si...
ABSTP CT:K comprehensive theory of stochastic realization for multivariate stationary Gaussian proce...
Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks t...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
Abstract—We discuss the problem of generating re-alizations of length N from a Gaussian stationary p...
The paper reviews several methods for the generation of stationary realizations of sampled time hist...
Matrix spectral factorization is traditionally described as finding spectral factors having a fixed ...
The purpose of this paper is to extend the deterministic behavioural theory of J.C. Willems to a sto...
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
In this paper we consider the problem of obtaining a state space realization of a zero mean gaussian...
A new procedure for stochastic realization of spectral density matrices is briefly reviewed. Afterwa...
A spectral density matrix estimator for stationary stochastic vector processes is studied. As the du...
A spectral density matrix estimator for stationary stochastic vector processes is studied, As the du...
Abstract---We discuss the problem of generating realizations of length N from a Gaussian stationary ...
The subject of modelling and application of stochastic processes is too vast to be exhausted in a si...
ABSTP CT:K comprehensive theory of stochastic realization for multivariate stationary Gaussian proce...
Given a spectral density matrix or, equivalently, a real autocovariance sequence, the author seeks t...
State-space representations of Gaussian process regression use Kalman filtering and smoothing theory...
Abstract—We discuss the problem of generating re-alizations of length N from a Gaussian stationary p...
The paper reviews several methods for the generation of stationary realizations of sampled time hist...
Matrix spectral factorization is traditionally described as finding spectral factors having a fixed ...
The purpose of this paper is to extend the deterministic behavioural theory of J.C. Willems to a sto...
The paper addresses the problem to estimate the power spectral density of an ARMA zero mean Gaussian...