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The merit of this approximation is that it is somewhat easier to implement and compute in practice.
The merit of this approximation is that it neither imposes any rigid functional form of specification on the structure of utility function nor assumes a specific form of the demand system, for example, a double-log demand model.
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The prime merit of this new approximation is to provide a very efficient yet accurate solution to the linear transport equation by substantially reducing the spatial mesh size.
The merit of the continuous approximation is that we may predict the lithium storage capacity for graphene sheets of any size, which could be computationally challenging using molecular orbital calculations.
The consequences of this approximation are minimized by three factors.
Data source triangulation is a merit of this study.
Our results further suggest the merit of this.
Also see the review papers by Heller [36] and Kay [41] where the respective merits of various semiclassical approximation methods are discussed.
The merit of the mean field approximation over stochastic simulation lies in its potential to reduce expensive computation times involved in simulation.
Whatever the merits of this argument, three things seem clear.
The cumulative merits of this system are as follows.
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