Tables A.2, A.3 and A4 in Additional file 1 show the selected scenarios after modifying costs, length of follow-up and the ratio of non-invasive tests among screened and background strategies.
For example, if multiple resistance genes or alleles with different pleiotropic effects occur in a pest metapopulation, different strategies for modifying costs might be needed to efficiently delay resistance in different regions.
It should be noted that this modified cost matrix is consistent with the total cost calculations as per Section 3.3.
The following slightly modified cost function was used to replace Equation 3 J x = y - Ax 2 2 + λ ∑ k = 1 K f k 2 + ε 1 / 2 (4).
The analysis of this modified cost function is then presented followed by a sample-by-sample stochastic gradient algorithm to optimally compute the analytical solution.
The second component consists of computing the modified cost matrix from Section 3.4 for each of the applicable parameters in the grid-search, and each of its historical test sets.
Upon further investigation, it became clear that the performance degradation in such cases was due to noise coloring by the CSF, as addressed in Section 5. We now consider such an example, and show that the use of the modified cost function given in (4) results in flatter sparsening filters, and improves the BER performance.
Upon considering the discounted NPV in Section 3.3 and various costs from Section 3.4, we arrive at the following modified cost matrix: begin{aligned} CM_{mathrm{mod}}(r,t) = sum_{i} frac{1}{(1+r)^{t}} Biggl( begin{bmatrix} 0 & c_{fp}(i) c_{fn}(i) & 0 end{bmatrix} + bigl(c_{mathrm{Ext}}(i) + c_{mathrm{Model}}bigr) begin{bmatrix} 1 & 1 1 & 1 end{bmatrix} Biggr) bigg/ sum_{i}1.
The Hessian of this modified cost function is computed, and its eigenvalues and eigenvectors are studied.
In the present work, we refine the DCJ-indel [ 7] and the DCJ-substitution [ 8] models, by adopting a distinct content-modifying cost that is upper bounded by the DCJ cost.
