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When the firm maximizes this expression, we obtain an optimal wage of w = frac{{p + w^{ circ } - b}}{2}.
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We can, however, maximize this expression iteratively using the EM algorithm [23].
Estimating v involves maximizing this expression, giving v = n / T. Thus the maximum-likelihood estimate of the rate for a constant Poisson process is the average rate of the observed events.
Maximizing this expression with respect to the distribution's parameters μ and σ gives νT lognormally distributed with the mean and standard deviation of log ( ν T ) equal to − 0.10 ± 0.04 and 2.43 ± 0.02, respectively.
By maximizing this expression, the ML condition in (27) is minimized, which results in the metric τ ̂ CCE = argmax τ ~ ∑ i ∈ { 0, 2 } ∑ k ∈ K 2 ( b ~ k, i e − jπ ν ̂ i ) ( b k, i e − j 2 π T k τ ~ ) ∗ (32).
To maximize this expression, we write it in matrix notation: The problem of finding w J and w K is simply solved by SVD on Z[ 22, 40].
Ideally, given two proteins, we would like to find a matching f that maximizes the expression for d0 a fixed constant (we shall discuss details about this constant later).
An n-Fekete set corresponding to Q is a subset {zn1,…,znn} of C which maximizes the expression ∏ni
where denotes the function that returns the argument that maximizes the expression.
To further simplify the expression, we find the value of (overline {alpha }_{k}) which maximizes the expression in (18).
Nevertheless, a good set of signature vectors can be found by generating a large set of random signature vectors and picking the one that maximizes the expression in (20).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com