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The same remarks we make on Batir and Cancan's assertion [[1], Theorem 2.6], which is proven to have some computation errors, since the expression ( 1 + 1 / n ) n + 1 can be approximated for large values of n as ( 1 + 1 n ) n + 1 ≈ exp ( 1 + n 2 ( n + 1 6 ) 2 ), (4).
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Assuming that the distribution of may be approximated by a Gaussian for large, we only need its mean and variance to characterize it.
Reliabilities of prediction also have to be approximated because direct matrix inversion is not feasible for large datasets.
Clearly, frequency response (14) can only be approximated by a FIR filter, but the approximation is acceptable for large.
When is large, (D.4) can be approximated by (D.5).
Therefore, for sufficiently large values of, (27) can be approximated by (28).
For large numbers of proteins, these processes can be approximated by deterministic mathematical models, such as those considered in this paper.
For larger densities of 20 or 35kg/m3fD can be approximated as 0.8 or 0.4, respectively.
For large samples the difference between the paired proportions can be approximated to a Normal distribution.
Therefore, for large M (and thus N), the inverse covariance matrix can be approximated as (62).
Due to large sizes of genomes, this can be approximated by a Poisson distribution.
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