An important caveat is that real-world banks fall far short of the perfect asset-side diversification of risks they adopt under the idealized conditions we study.
Under the idealized conditions of perfect models and precipitation input, one may expect that spatiotemporal specificity and accuracy of the model output improve as the resolution of the models and precipitation input increases.
The IBD-GS method is thus easier to implement for analysis of selectively genotyped and sparse marker data and of phenotypic data that do not necessarily fit the idealized conditions that are typically assumed in stochastic simulation studies.
Second, while maximum likelihood can be a very accurate phylogeny estimator when the sequences evolve under the model assumed in the ML software, true biological datasets do not evolve under the idealized conditions reflected in even the most complex DNA sequence evolution models used in this experiment.
Thus, the IBD-GS method is suitable for cost-effective genomic evaluation using sparse markers, for selective genotyping of training animals, and for phenotypic data that do not necessarily fit the idealized conditions that are typically assumed in stochastic simulation studies.
Under these idealized conditions, eventually all cells commit to the islet cell lineage.
First, the results for a 2D case with uniformly distributed premixed injection are presented to characterize the RD propagation under the most idealized conditions.
We show that, under these idealized conditions, high bank leverage is optimal when there is a market premium for the production of (socially valuable) liquid claims.
Numerical methods typically yield approximate solutions to the governing equation through the discretization of space and time and can relax the rigid idealized conditions of analytical models or lumped-parameter models.
Deterministic, distributed-parameter, numerical models can relax the rigid idealized conditions of analytical models or lumped-parameter models, and they can therefore be more realistic and flexible for simulating field conditions.
