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We have approximation properties of the operators via a universal Korovkin-type theorem and a weighted Korovkin-type theorem.
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At the end, we have an approximation of S max.
Under the NMF model, we have the approximation mathbf{Y} approx mathbf{S}mathbf{A}, (7).
Moreover, we have the approximation of solutions (x_{n}) as (n=1, 2,ldots ) for equation (1.1).
We continue this recursive process until we have an approximation of the pre-image with an error smaller than ε.
Therefore, we have the approximation that { cos θ t ≈ 1 sin θ t ≈ ω eff ⋅ t (6).
In this work, we have improved approximation for double derivative by taking one more term in Taylor series expansion.
Then by the standard likelihood based analysis, we have the approximation ψ ̂ − ψ 0 = n − 1 ∑ i = 1 n I ψ − 1 S i ψ + o p n − 1 / 2, (11).
Thus we have the approximation :\log_2 x) \approx e_x + m_x + \sigma.
From Lemma 4, for large m, M, and v, we have the approximations (x_{m} approxfrac{sqrt{12}-4}{2m} approxfrac{sqrt{12}-4}{2m}{12}-4}{2M}).
We have used approximations to weight transition probabilities, adjusting only for differences in weights of individuals with whom there are shared ties.
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