The phrase "a more accurate approximation" is correct and usable in written English.
It can be used when discussing the precision of a measurement or estimate in comparison to another.
Example: "After recalculating the data, we found that our previous estimate was off, and we now have a more accurate approximation of the project's costs."
Alternatives: "a closer estimate" or "a better approximation".
Given the functions f 1(X p,Y p ) and f 2(X p,Y p ), we define the derivative matrix D, the current approximate result ({vec {psi }_{k}}), and a more accurate approximation ({vec {psi }_{k+1}}) to compute ({vec {psi }_{p}}).
This one is a more accurate approximation, but is somewhat slower.
A more accurate approximation, especially useful in case of normal threshold voltage distributions, can be obtained by a Gaussian fit of the single cell transconductance.
Numerical results using goal-oriented hp-FEM simulations show that our post-processing method provides a more accurate approximation to the actual formation resistivity than that predicted by Kaufman's theory.
Since the Laplace distribution recently justified to represent jitter in the CNA breakpoints is not sufficiently accurate to estimate small changes, we propose a more accurate approximation based on the modified Bessel function of the second kind and zeroth order.
This new methodology allowed the researchers to observe the actual behavior of drivers and to develop more accurate operating speed models than was previously possible with spot-speed data collection, thereby enabling a more accurate approximation to the real phenomenon and thus a better consistency measurement.
Therefore, given any fixed truncation n φ = Λ = G, a polynomial expansion gives a much more accurate approximation of the true density φ(x).
In the numerical results, we show that the AEGP is a far more accurate approximation of the EGP, compared to the metrics used in [19, 20].
Since the impulse response of linear transformation is difficult to compute without prior knowledge, we propose a simpler and more accurate approximation algorithm.
While a number of publications have presented methods by which one can analyze RNA-Seq data (many of which are reviewed in [ 11]) and account for unknown covariates [ 12- 15], the steps we present herein ultimately provide a straightforward approach that allows for more accurate approximation of gene expression values that can be confidently used in downstream disease-based comparisons.
These non-linear models typically achieve more accurate approximation to the factor response relationship than traditional polynomial regressions.
