Sentence examples for methods for random from inspiring English sources

Xiu, J. Shen, Efficient stochastic Galerkin methods for random diffusion equations, J. Comput.

Comparing the results with traditional Monte Carlo simulation, two numerical examples verify the remarkable accuracy and effectiveness of the proposed methods for random temperature field prediction in engineering.

The experimental results demonstrated that the weighted total least squares method is more effective than other methods for random dynamic load identification.

This non-parametric approach differs from the parametric methods for random uncertainties modelling and has been developed in introducing a new ensemble of random matrices constituted of symmetric positive-definite real random matrices.

Given this deficiency, alternative methods for random effects meta-analysis have been proposed.

One possibility mentioned in the discussion is adapting nonparametric Bayesian methods for random effects models to this setting (for discussion, see [ 40]).

Previous authors have considered this issue for updating of meta-analyses assuming fixed effect [ 30] and heterogeneous data accumulation, [ 31] (Higgins JPT, Whitehead A, Simmonds M: Sequential methods for random-effects meta-analyses, submitted) adapting formal monitoring procedures initially developed for single RCTs.

Present study focuses an efficient stochastic finite element method for random response sensitivity analysis.

Assuming appropriate density probability functions for each random variable and adopting the Latin Hypercube Sampling (LHS) method for random sampling, the input data set has been defined.

This study adopts the acceptance-rejection sampling method, instead of the direct sampling method, for random variable sampling to reduce computing complexity.

The combined method can considerably reduce the time-consuming VOF domain, and thus practically makes it possible to apply the VOF method for random wave calculations, in which long time computations are usually required to obtain statistically meaningful results, and therefore the use of the single-VOF model often becomes prohibitive in terms of computational time and storage memories.