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We base our modelling on the standard random effects model [ 7- 10] for meta-analysis, (1) y = μ 1 n + u + e, where y is a n-vector of estimated treatment effects for the n independent studies, μ is the unknown overall treatment effect, 1 n is a n-vector of ones, u is a n-vector of unknown random effects, u ~ N 0, τ I n) where τ2 is the between-study variance which is unknown.
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We base our model on the Naming Game [2, 4, 32 34].
We base our models on Population Processes, an approach commonly used to represent the dynamics of biological populations.
Rather than applying one of the classical academical or commercial numerical finite volume (FV), finite difference (FD) or finite element (FE) interface tracking methods, we base our models on an interface capturing phase field method (Nestler, 2005).
We will base our model on the circuitry of the known biochemical network, only.
We base our model on data that are obtained from large-scale studies of IgG substitution therapies [15].
We therefore base our model on the Lockhart equation and use it to replace Darcy's law that had been at the base of an earlier model of pollen tube growth [34].
While Ogale & Aloimonos use a geometric approach to find the corresponding regions, we base our model on biologically inspired processing stages that work in parallel, but share some of their information due to modulatory connections.
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We model individuals that are diploid hermaphrodites (i.e. we do not distinguish between males and females, albeit we base our model on female life history).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com