Sentence examples for variable models was from inspiring English sources

A range of multiple variable models was constructed using the estimation dataset with a combination of these variables depending upon their importance based on adjusted R2 values.

One of the multiple variable models was chosen as the mapping model based upon explanation of the maximum amount of variability in the EQ-5D index with the fewest variables, as well as relatively low RMSE and MAE values.

Competing latent variable models were identified in previous studies.

Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R2 (R2α) and AIC.

However, estimation of such lagged dependent variable models are, in general, inconsistent in the presence of fixed child or family effects.

All weather IMFs identified in single weather variable models were then selected in the final model of regression analysis (i.e., multiple weather variables model) to compute the most significant predictors of headache incidence.

Discrete and continuous latent variable models were used to identify knowledge flaws and the target groups.

Latent variable models are frequently characterized by poorly defined, multimodal likelihood functions, and this appears to be the case here.

Latent variable models are a group of methods that use the information from the manifest variables to identify subtypes of cases defined by the latent variable.

The tool is built using a probabilistic matrix factorization method and DrugBank v3, and the latent variable models are trained using the GraphLab collaborative filtering toolkit.

To establish whether differences in language groups influence QoL scores, both univariate and multiple variable models were used to examine unadjusted and adjusted effects on QoL.