Sentence examples for matrix because they from inspiring English sources

The alternatives we consider to quantify the information content of a stated preference study are known to have better finite sample properties than the Fisher information matrix, because they are based on Bayesian estimation procedures that are considered more appropriate than maximum likelihood procedures when the sample size is small.

The main experimental problem associated with the proposed organic:organic "host:guest" system is the incorporation of the maleimidic monomers in the polymeric matrix, because they show processing limitations determined by the solubility and fusibility properties [39 43].

Recall that, of all the FS nanoparticles investigated here, the FS-MA nanoparticles are expected to be the most uniformly dispersed throughout the copolymer matrix, because they promote the greatest and most consistent property enhancement.

Two men and one woman were not included in this correlation matrix because they did not excrete 7-OHC.

Note that metabolites that are held at a fixed concentration (boundary metabolites) do not enter the stoichiometry matrix because they do not have a rate of change.

The short AGL6-sequences from Asarum, Berberis, Anemone, Papaver, Citrus and Eschscholzia were not included in this first matrix because they can be expected to artificially lower support values in resampling methods such as bootstrapping.

We call Π the preconditioning matrix, because, as it will be explained in Section  3.2, it is used to increase the convergence rate of the recursions (8).

This is especially true of random measurement matrices, because they are computationally expensive and require considerable memory [3].

It can be seen that symbols (left[ {mathbf{M}} right]), (left[ {mathbf{C}} right]) and (left[ {mathbf{K}} right]) in Eq. (20) are called instantaneous matrices because they are time-dependent matrices due to the position of the moving oscillator.

In a graphical Gaussian models context, (i) is simply achieved by comparing the two concentration matrices (inverse of the covariance matrices), because they contain all the information about the underlying structure of conditional independences among variables.

In a graphical Gaussian models context this is simply achieved by comparing the two concentration matrices (inverse of the covariance matrices), because they contain all the information about the underlying structure.