Sentence examples for bic model from inspiring English sources

b) Results of lowest BIC model selection approach using an exhaustive search of all potential model permutations.

Bayesian Information Criterion is measured using the following equation: BIC (model)= -2 * log (Maximum Likelihood (model))+log(N) * N_{parm} (11).

Then, using semi-empirical engineering scaling criteria (constant solvent lineal velocity or constant solvent residence time) and the Broken and Intact Cell (BIC) model, the scaling up to larger extraction vessels (1.35 × 10−3 m3 and 5.16 × 10−3 m3) was theoretically investigated.

While the unidimensional shows a deviance (equals −2 Log-Likelihood) of 16,178.8, the multidimensional model shows a deviance of 16,058.6, which results in a significant Chi Square test (df = 9) as well as AIC and BIC model fit values (16,283 vs. 16,181 and 16,504 vs. 16,440, respectively) in favor of the multidimensional model.

Results of the per-gene BIC model selection for both the cell line and human brain datasets.

Third, Bayesian information criterion (BIC) model selection was used to score the ratio- and intensity-based linear models for each array feature.

Finally, the relative fit of the non-nested SEM models (M2, M3, and M4) was compared using the AIC and BIC model-fit indices.

Unlike AIC and BIC, models must be nested (one model is a subset of another) and have different numbers of parameters.

Note that AIC prefers random intercept and slope model with UNS, IND and AR1 covariance structures, models 6, 4 and 5, respectively, whereas random intercept and slope model with IND and AR1 covariance structures are to be preferred based on BIC, models 4 and 5, respectively.

The BIC of model 2.2 is actually higher than model 2.0, which contains only the national election indicator.

While AIC is also reduced, the BIC of model 1.2 is actually increased with respect to model 1.1 (albeit fractionally).