Sentence examples for model preference was from inspiring English sources

Model preference was assessed for the noiseless TACs, as well as for the different noise levels.

When 5% noise was added, AIC for the 2TCM increased with larger k3 such that a small overlap in model preference was seen for the highest binding TAC.

Model preference was determined by Akaike information criterion (AIC), while parameter agreement was assessed by linear regression, repeated measurements ANOVA and Bland-Altman plots.

At first, a set of noiseless TACs (n = 10) was generated to assess whether model preference was associated to the level of specific binding relative to the non-specific signal present in that particular region.

However, this difference in model preference was mainly driven by a single subject where AICC disagreed strongly with the other model selection criteria, which was confirmed by the F-test.

The model preference is thereby based on the correlation coefficients of several selected independent variables against production performance.

For all the tracers, a variation in the model preference is observed with varying VOI size, which is primarily due to noise.

Due to the similarity in model preference between PS group and low-density regions of the TD group, it was hypothesized that model preference is affected by the levels of specific binding present in a particular region.

In the Tubiana stage model, greater preference was placed on the ring finger.

Occasionally, other models were preferred, but this preference was never significantly greater than the gaze-related models.

In the mixed model regression analyses, patient preference was the dependent variable, all potential predictors were included as fixed effects and centre was included as a random effect.