Sentence examples for matrices of multivariate from inspiring English sources

This paper also, investigates a new and simple method for adaptively refining the covariance matrices of multivariate Gaussian distributions used in the proposed updating strategies.

Doğu and Kocakoc (2011) estimated the time of step change in the covariance matrix of multivariate normal processes, in which a multivariate control chart based on sample covariance is used for receiving out-of-control signals.

Finally, in order to obtain model parameters for all possible chords related to a given chord type, another circular permutation is applied on mean vectors and the covariance matrix of multivariate Gaussians.

This paper presents the analytical derivation of joint probability density functions (pdfs) of the maximum likelihood (ML) estimates of a real and complex persymmetric correlation matrices (PCM) of multivariate Gaussian processes.

Suppose that the quality characteristics u and v are the sources of variation in the covariance matrix of multivariate-attribute quality characteristics.

The results of the simulation study show that the proposed ANN-based estimator outperforms the MLE estimator under different out-of-control scenarios where different shift magnitudes in the covariance matrix of multivariate-attribute quality characteristics are manifested.

As the main contribution in this paper, we propose a modular methodology based on artificial neural networks for estimating the step changes in the covariance matrix of multivariate-attribute processes.

Let also after incidence of a given step shift in process variability, the covariance matrix of multivariate-attribute quality characteristics changes from Σ 0 to Σ 1 at the unknown change point τ.

Then, based on the out-of-control state which is diagnosed in the first module, one of the designed ANN estimators in the second module will be activated for estimating the change point in the covariance matrix of multivariate-attribute quality characteristics.

The multivariate statistical analysis of random processes widely uses the Wishart distribution, which describes statistical properties of a maximum likelihood (ML) estimate of the real-valued positively definite correlation matrix (CM) of multivariate Gaussian processes/fields [1 5].

Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits.