Sentence examples for modeling methods require from inspiring English sources

Almost all proposed modeling methods require tuned adjustable parameters which have been optimized based on experimental data.

Many modeling methods require large amounts of historical data.

Modeling methods require good knowledge of the X-ray spectrum and all materials in the X-ray beam path from source to detector, including characterization of the performance of the antiscatter grid and detector.

Application of the mixed model method requires data sets in which all individuals are measured for genome-wide genotypes and the environmental risk factor.

The mixed linear model method requires large samples measured for both individual level genetic and environmental measures (based on power considerations (e.g., Dudbridge, 2013; Visscher et al. 2014), we estimate a sample size of at least 5000 individuals).

Currently available model updating methods require construction of a detailed initial FE model in order to achieve convergence of the modes obtained from updated FE model to their experimental counterparts.

First, while there are theoretical statistical models for estimating the effects of treatment switching such as marginal structural models, these methods require the strong assumption that there are no unmeasured confounders and typically require detailed data on time-varying confounders, which are unlikely to be available in the CPRD.

Conversely, model-free methods require several kinetic curves to perform the analysis.

Model-driven performance prediction methods require detailed design models to evaluate the performance of software systems during early development stages.

These usually use two approaches: methods using consensus of many alternative models and methods requiring only a single model to do its prediction.

However, prognostic models, particularly probabilistic-based methods, require a large number of failure instances.