Sentence examples for model methods are from inspiring English sources

The probabilistic model methods are Bayesian reasoning, evidence theory, robust statistics, recursive operators.

It is found that both model methods are capable of reflecting the essential dynamical characteristics but there are some differences in system eigenfrequencies and response amplitudes.

Furthermore, the numerical observation of a case study for a 6 degree-of-freedom system also suggests that first-order state-space model methods are more robust and accurate for the estimation of modal frequency and damping.

We and others [ 3, 5, 6] have shown that mixed model methods are a powerful yet parsimonious approach for analyzing microarray data.

More traditional variance methods (such as mixed model methods) are difficult to implement in this context because of the need for these approaches to put all of the parameters simultaneously in the models, which results in over-fitting.

With sufficient data and under the necessary assumptions, the dynamic marginal structural model methods are unbiased, as we showed in simulations of very large observational studies (n = 100,000 participants).

A total of 28 models (14 data sources × two model methods) were constructed using data from 433 plots.

A total of 32 models (16 data sources × two model methods) were constructed using data from 484 plots.

Mixed model methods were used for the analysis, with separate variances fit for each of the 2 groups (SC and C) when necessary.

Because of groundwater conceptualization uncertainty, multi-model methods are usually used and the corresponding uncertainties are estimated by integrating Markov Chain Monte Carlo (MCMC) and Bayesian model averaging (BMA) methods.

Generative Modelling methods are becoming more popular.