The accuracy of GP surrogate models is quantified by a confidence level measure and continuously improved through the sequential adaptive sampling.
The accuracy of the models is quantified using bias statistics where bias is defined as the ratio of measured pullout capacity to predicted value.
The accuracy of the analytical models is quantified by considering realistic three-dimensional microstructures containing curved dislocations with a specified distribution.
The performance of the proposed category of models is quantified through a series of experiments, in which we use two machine learning data sets and two publicly available software development effort data.
The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale.
The goodness of fit of noise models is quantified by a hierarchical Bayesian analysis of variance model, which predicts normalized expression values as a mixture of a Gaussian density and t-distributions with adjustable degrees of freedom.
