Sentence examples for finite data are from inspiring English sources

First, it is not clear whether BI using a Bayesian decision rule is significantly biased when finite data are analyzed, when the true tree is resolved, or when sequences generated under realistic conditions are analyzed.

Finally we note that some stability results (quite different type, so-called stability for finite data) were obtained by Marlettta et al. [21].

Note that boundary effects inherent for finite data were corrected for, by sub-weighting extreme values appropriately.

The qualitative behaviors of the eigenvalues and eigenfunctions are discussed, and numerical reconstructions of the potential with a Newton method from finite spectral data are presented.

Up to now, the steps of the proposed matrix reconstruction method with the finite sampling data are summarized as follows: (1) Calculate the column vectors ( {mathbf{r}}_{{mathbf{X}}^k{left({x}_k^kright)}^{ast}}^k ) of the equivalent auto-covariance matrix R xx by (6) and (9).

The quantification of the finite element data is further performed by an optimization approach of GP.

A comparison with experimental and finite element data is conducted and a design to damp out these lateral modes is investigated.

Kassi et al. [ 27] pointed out that the stair climbing load is more detrimental than single stance; however, Petterson et al. [ 11, 12] demonstrated that in general both physiological loadings showed similar stress shielding patterns when finite element data was compared with the experimental data.

Since structures are subject to unknown ambient excitation in operation conditions, all estimates from the finite data measurements are of statistical nature.

Moreover, the resulting BF is independent of the measurement scale of the dependent variable, is always finite for finite data, and is consistent in the sense that as sample size increases, BF grows to infinity if the null is false and shrinks to zero if it is true a consistency that does not obtain for p values.

where U S is the signal subspace spanned by eigenvectors corresponding to major eigenvalues of matrix R, U N is the noise subspace spanned by eigenvectors corresponding to small eigenvalues of matrix R. In practical calculation, the received data are finite, so the covariance matrix R can be estimated as R ^ = 1 L ∑ i = 1 L Z t Z H t, (5).