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Applying of our method to the global ionosphere maps allows us to obtain additional information about the ionosphere plasma redistribution.
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In addition, the general disadvantages of time series analysis apply to our method (see for example Chatfield 2004).
One can also imagine applying an iterative application of our method to combine several forecast models of different types using the differential probability gain method.
Monte-Carlo simulation was applied to demonstrate the validity of our method.
We analyze the rate of convergence of our method applied to the average consensus problem.
We present results of our method applied to three human pathogens: Escherichia coli, Mycobacterium tuberculosis and Staphylococcus aureus.
To illustrate the neural response calculation, let us show an example of our method applied to a specific neuron.
The algorithms for generating the data structures of our method apply not just to Gillespie algorithm, but to any discrete event simulator.
The jackknife test, which is deemed to be the most objective and rigorous protocol for evaluating predictive bioinformatics methods, was applied in evaluating the performance of our method [ 39].
As a demonstration of our method applied to real data, we used a case/control study of severe malaria from The Gambia, genotyped on the Affymetrix 500K array (data provided by the MalariaGEN consortium, www.MalariaGEN.net).
Instead of applying our method to n participants, it can be applied to them batch by batch.
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