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Traditionally, these are simulated using mesh-based methods formulated in an Eulerian frame of reference.
We observe that different kinds of secondary structure prediction outputs (single-state prediction, single-state prediction with a confidence index, and three-state probability prediction) do not differ greatly in the amount of structural information they yield, so long as the methods formulated in this work to generate propensity distributions are applied appropriately.
We demonstrate, by simulation, that the multinomial regression approach has greater power to detect disease association, in the presence of heterogeneity in allelic odds ratios between sub-phenotypes, than do existing methods formulated in a logistic regression framework.
First, the multinomial regression-based analysis performs well in comparison to existing methods formulated in a logistic regression framework over a range of models incorporating heterogeneity of genetic effects between sub-phenotypes.
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Below we detail the convergence-constraint design method formulated in (5).
The evaluation method, formulated in our study, can be used to other areas with similar conditions.
Despite the similarity, the scheme we develop here does not coincide with any Rosenbrock type method formulated in [24, 27].
The main tool is the multiple-correction method formulated in (Cao et al. in J. Number Theory 149 327-350, 2015; Cao in J. Math. Anal. Appl. 424:142015446, 2015).
We began by assuming a rectangular fault model in an elastic half-space (Okada [1985]) and then applied the nonlinear inversion method formulated in Matsu'ura and Hasegawa ([1987]).
In this section, we will derive the basic reproduction number of (2.2) by using the next generation matrix method formulated in [17, 18].
The material point method is a variant of the finite element method formulated in an arbitrary Lagrangian Eulerian description of motion.
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