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One approach could be using the ML estimation [39, 40].
Then, using the ML estimation criterion, (hat {u}) is obtained from [7] begin{array}{*{20}l} hat{u} &= underset{u}{argmin} leftVertboldsymbol{Y_{c}} - boldsymbol{hat{H}_{c}}boldsymbol{S}_{u}rightVert^{2}.
Validity was assessed using the known-group comparison (229 with vs. 823 without chronic illness), item-domain correlations, and CFA using the ML estimation in LISREL.
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Ancestral state estimation is congruent when using the ML topology (Additional file 3, Figure S1), with one exception.
Then, an advanced timing, relative timing indices, and the corresponding CIR estimates at the second stage are obtained using the generalized ML estimation based on a sliding observation vector.
While both methods appeared to be effective, a slightly more accurate result is obtained using the direct ML estimation technique in this case.
The second step is to use the maximum-likelihood (ML) estimation method [ 16] to determine associated parameters (combination strength) in (1) by the microarray expression data as follows (see Additional file 2): (2) x i (n ) = ∑ j = 1 M i α ^ i j x j (n ) where α ^ i j was determined by using microarray expression data and the ML estimation method.
For example θ ^ M L { l 2, r 4, c 6 } is the ML estimation resulting from using the left shifted distribution at time t1 = 2, the right shifted distribution at time t2 = 4, and the center distribution at time t3 = 6.
The ML estimation of can be performed using (17).
Thus, all parameter estimates were obtained using restricted ML estimation.
The ML estimations have been done using the Parameter Estimation Toolbox for Systems Biology developed by the Systems Biology group at the Fraunhofer Chalmers Centre (FCC) in Göteborg (Sweden).
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