Sentence examples for model simplification by from inspiring English sources

For instance, Ullah, Bruno and Pearson [54] proposed model simplification by the elimination of nodes with low equilibrium occupancy probability using time scale separation arguments.

However, the issue with the model simplification by pruning correlated parameters seems to differ between the response and the main text.

The final models were selected by stepwise model simplification by comparing Akaike information criterion (AIC) values, and models with the smallest AIC values were selected as the minimum models (Crawley 2002).

The proposed penalty has two main features: (i) it automatically determines the order of the VAR model, i.e. the number of effective time lags and (ii) it performs model simplification by reducing the number of covariates in the model.

We followed model simplification by sequentially dropping the least significant term and comparing the change in deviance with and without the term to chi-squared distributions, until the minimal adequate model was reached.

We followed the principal of model simplification by removing terms to achieve a model with the smallest value for Akaike's Information Criterion (AICc, AIC corrected for small sample size), which weighs the goodness of fit of competing models against by the number of terms included.

We used a backward model simplification procedure by first fitting saturated models.

Model simplification proceeded by stepwise deletion of non-significant terms (P > 0.05).

Stepwise model simplification started by removing the most complex interactions, one at a time, to the simplest one, and F-tests or chi-squared tests were run to assess the significance of the increase in deviance that may result by removing a term from a model (Crawley 2007; Zuur et al. 2007).

It is a major advantage of the approach that the model simplification is performed by an automatic error control and that the simplified model is physically interpretable again.

Model simplification was performed by deleting the least significant term and comparing the log likelihoods of the nested models using ANOVA [46].