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In the implementation, we set the value of the tolerance to be (varepsilon =10^{-6}) for all the examples.
It has an ideal value of 1 (and hence the ideal value of the tolerance factor is also 1).
A thorough numerical study is carried out which shows that a strict error control is achieved and that the method exhibits an optimal computational complexity which does not depend on the value of the tolerance or on the type of Hamiltonian.
By determining the minimum value of the tolerance factor, ((x_{r}, x_{s} )) is our desired nonparametric tolerance interval for the project performance in the ith review period with the confidence level of (1 - alpha) that covers (p times 100%) of the population.
In the formulation presented in equation (2), the only user defined parameter is the value of the tolerance which dictates how closely the model prediction must agree with experimental dynamics in order for the network to be considered in the overall algorithm.
Similar(55)
The values of the tolerance level (TOL) and the variance inflation factor (VIF) are shown in Table 2.
These limit values of the tolerance are used to create the bar in Figure 10A.
Mean and 95% confidence intervals of pAUCs across simulation runs were calculated for each method for five values of the tolerance parameter (1, 2, 5, 10 and 20).
To determine the value of the prescribed tolerance, we examined two possibilities: a tolerance of 0.1 μM and a tolerance of 0.01 μM.
We performed several experiments to select the best value of this tolerance.
The method provides automatic solutions with an accuracy that can be effectively set up by choosing an appropriate value of the error tolerance parameter.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com