Sentence examples for models not converging from inspiring English sources

Indications of poorly trained models include poor recognition performance, models not converging during Baum-Welch reestimation, and model parameters not moving significantly from initial values (typically from a flat start).

Data for many analyses were sparse, which resulted in many models not converging.

Model not converging due to small numbers.

The main problem with this omission is that continuous variables are a potential cause for model misbehavior, that is, the log-binomial model not converging, and the Poisson model producing estimates of individual probabilities greater than 1.

With such a small training set (20 samples of a gesture), the fact that random initialisation is not as good as informed initialisation suggests the models are not converging.

In a few instances, the models did not converge and log-Poisson models, which provide consistent but not fully efficient estimates of the RR and its confidence intervals, were used [ 43].

Logistic regression modeling was attempted, but the models did not converge.

As appears from table 3, several of the models did not converge with observer included in the model.

Note: **: The c-statistic is significantly different from the c-statistic of CMS-HCC model at the 5% level; NA: Models did not converge due to complete (or quasi-complete) separation.

Models used unstructured correlation or exchangeable correlation where models did not converge using the unstructured matrix.

23 In some instances, the models did not converge and we therefore used log-Poisson models, which provide consistent but not fully efficient estimates of the PRs.