Sentence examples for model selection involves from inspiring English sources

Given a data set, model selection involves finding the statistical model that best captures the properties and regularities present in the data.

This is because model selection involves cryptic multiple hypothesis testing, a fact that has only rarely been acknowledged or quantified.

Each modeling regime was fit independently with a resulting likelihood used for model selection involving Akaike information criterion (AIC).

The Bayesian model selection involving the use of the model evidence is always valid, contrary to the Bayesian/Akaike Information Criterion, which are approximations valid in the limit of using large amounts of data.

This model selection step involves a significant runtime overhead and can be avoided if the number of sub-distributions can be estimated.

Model selection, therefore, involves a trade-off between bias and variance, reflecting the statistical principle of parsimony (Burnham and Anderson 2004).

Our methodology relies on the natural selective nature of sparse representation in order to perform classification wherein no model selection is involved; with regards to robustness to outliers, our classification enabled us to discard bad training samples and handle noisy data [ 8, 20].

The fundamental difficulty with inference in nontrivial extrapolation where model selection is involved from a rich space of models is that any model estimated in one regime used for decision making in another is fundamentally confounded with disruptive alternatives.

No model selection was involved in this step.

Since the ℓ1-minimization approach proposed here, where no model selection is involved, requires a more robust way of presenting the information to the algorithm, in this case, we conclude that the DPPS encoding is more appropriate.

Natural selection involves organisms trying to adapt.