The phrase "approximation will be" is correct and usable in written English.
It can be used when discussing future estimates or predictions regarding a value or outcome.
Example: "The approximation will be provided after we analyze the data collected from the experiment."
Alternatives: "the estimate will be" or "the approximation is expected to be".
First, several concepts of approximation will be explored and their application in approximate reasoning will be discussed.
The type α + 1 approximation to a set will always be the set of type α approximations; if λ is a limit ordinal, the type λ approximation will be the sequence {sβ }β < λ of approximants to the set at earlier levels (so our "intended model" above is the set of type ω approximations in a larger model).
This analytical approximation will be used to explain the detector location effect through quantitative evaluation of a specific model.
A general model for knowledge-based decision making will be presented, and the special cases as applicable to some common concepts of approximation will be studied.
To this purpose, a suitable initial approximation will be introduced, in addition, the residual error in several points of the interest interval (RECP) will be canceled.
The direct meshless local Petrov Galerkin method is a newly developed modification of the meshless local Petrov Galerkin method that any linear functional of moving least squares approximation will be only done on its basis functions.
Low integer order discrete approximations will be used as models to design the controllers.
Such estimation of the function (cos^{2n}{x}) and the use of corresponding Taylor approximations will be the object of future research.
We consider the asymptotic case where each (bar {gamma }_{i} to infty ) for i∈{1,2,3}, and fading models where the diversity gain coefficient of the asymptotic approximations will be 1 – thus t=0 in the models from Table 2.
Generally, if n is greater than 25, these approximations will be good.
Normal approximations will be used, with p=0.05 as a threshold for statistical significance.
