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We thoroughly characterize our models by computing structural, electronic and vibrational properties.
Further insights of the kinetic and mass transport contributions of this system were obtained for both models by computing some variables that cannot be experimentally measured (i.e. surface concentrations).
We compared models by computing the difference in the proportion of explained variation (adjusted for over-optimism induced by the inclusion of additional variables) in the larger model compared to the smaller model, ( varDelta {R}_{adj}^2 ), using the str2d function in Stata (Royston 2006).
The novel approach taken in this study was to perform the optimal normalising transformation of variables by applying the power-transformation with parameter λ obtained with the help of a Box Cox transformation, and to compare the transformed and non-transformed ρb models by computing the 'corrected' Akaike's Information Criterion (AICcorr).
We did this for all three neuron/network models by computing the difference between the mean couplings of both regions.
We furthermore objectively and quantitatively assessed the applicability domains of the models by computing prediction confidence and domain extrapolation for predicting unknown chemicals with an extensive cross-validation.
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For this purpose, the variants of model by computing program Scia Engineering have been created.
For this purpose the variants of model by computing program Scia engineering has been created.
The same concept can however be generalised to any single events model, by computing molecular or lumped reactivities.
Flow starvation can be modeled by computing conditional packet loss probability and busy time duration sensed by each station in the network.
We propose to apply the diffusion framework to estimate an HMM model, by computing a reduced dimensionality representation of the training set and estimating the meta-states.
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