Sentence examples for model order to from inspiring English sources

Preliminary research showed us that there are also only small differences in model perplexity if we increase the model order to 4-gram models.

Singular value criterion (SVC), instead of information criteria like AIC and BIC, will efficiently give a more proper model order to make the estimates more accurate.

Firstly, the range of model order to be estimated is specified; then starting from the lower bound, the poles (eigenvalues) corresponding to a given model order n are computed and compared to the poles (eigenvalues) of a one-order-lower model.

It was shown that sub-word-based models require higher model orders to achieve performance comparable with word-based models [14].

Since spikes are sparse signals, using higher resolution data requires large model orders to produce non-zero Granger causality.

Since there is no specific criterion to guide the choice of model orders to test, we selected orders 8, 12, and 16.

Accordingly, we do not use the HH model in order to model or mimic the Parkinsonian condition.

For this reason, we use a truncated Gaussian mixture model in order to indirectly model the peak.

A model order reduction to diminish the system size is also possible.

The order of 5 was selected because the AIC dropped monotonically with increasing model order up to 5. The Granger causality spectra were then calculated.

Then the application of parametric model order reduction to the CME is introduced.