Sentence examples for model methodology with from inspiring English sources

Gengler et al. [ 9] suggested that missing genotypes could be modelled using the usual mixed model methodology with relationship matrix A. We now combine that idea with the genomic relationship matrix on the form (1).

This is in essence the same as the 'gene content' for a bi-allelic locus and the same mixed model methodology with the additive genetic relationship matrix can be applied to predict the nhc for each haplotype for the ungenotyped animals.

This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem.

We report on a combination of system theoretic simulation modeling methodology with the JavaBeans component model as a basis for a component-based simulation framework.

A GPU-based DEM modeling methodology with rolling resistance is employed to study comprehensively dip-slip faulting through granular soils from both engineering and fundamental viewpoints.

Clinical effectiveness data were analyzed in patients who had a non-missing baseline and at least one postbaseline assessment for a given effectiveness measurement and used mixed-model methodology with baseline value as a covariate, a fixed effect for time, and a random effect for center.

This article aims to fill this gap, and for the first time will present a method to calculate CIs for the new AAFs modelling methodology used in the 2005 CRA study for chronic diseases by region, sex and age (see [ 4] for a description of the AAF modelling methodology and [ 10] for a comparison of the new AAF modelling methodology with previous methods).

Genetic variability and selection response were estimated either with the mixed model methodology, or with the classical methods for calculating genetic gain, as the deviation from the control line, and the realized heritability, as the ratio of the selection response on cumulative selection differentials.

This course explores data modeling methodologies with the goal of understanding how to choose, apply, and interpret appropriate statistical designs and analyses for practical data problems.

We present our analysis in the following sequence: 1) the definition of codon-based models and an analysis of the simulation algorithms; 2) the effect of the update rules in codon-based models; 3) a new PBN model for mRNA translation; 4) a comparison of the different modelling methodologies with added biological complexities; and 5) discussion.

The usual mixed model methodology cannot accommodate models with unobservable incidence matrices.