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Exact(19)
This phenomenon has been verified in the simulations below, as shown in Fig. 14.
The probability of symbol error for QPSK modulation used in the simulations below can then be simply calculated using (22) and (23) for.
The simulations below will show, however, that the variation of event size for "small" genome sizes (Fig. 3) suffices to induce a shrinkage.
This estimator of the marginal likelihood is known to have poor performance in certain circumstances, although we empirically observe it to work well in the simulations below, as it has as in other phylogenetic contexts (Ronquist and Huelsenbeck 2003; Drummond and Rambaut 2007).
In the simulations below we study the impact of correlated inputs.
This is also the scenario the simulations (below) and power calculations ( Additional file 3) are designed to represent.
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All geometrical parameters in the simulation below are based on the International Technology Roadmap for Semiconductors (ITRS) 2007 [13].
In the simulation below, the free parameters related to KLMS are set as follows: the Gaussian kernel with kernel width equaling to 0.6 is selected and step size η is set to 0.9, which were cross-validated in a training set.
More specifically, the simulations described below will illustrate the possibility of providing answers for two questions: 1.
Note that an acquisition step is performed by the receiver but all the simulations presented below have been realised assuming that the receiver was in the "locked" state.
The simulation study below will use the data of the placebo group.
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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