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In order to model this variability, it is necessary to introduce non-determinism by allowing asynchronous execution of the model.
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We have modeled this variability as 25% random connections on top of the architecture obtained from the Gabor kernel (see Materials and Methods).
The data obtained from EC were used to calibrate the model, simulating this variability satisfactorily and even more-so when the model was forced during calibration.
Therefore a dynamic model capturing this variability was used to estimate the two infectiousness parameters.
Based on this model, the variability in the estimate of the mean fluorescence could be reduced by a factor of 25 by repeating the experiment over 3 days rather than 2; repetition of the experiment over 4 days further reduced variability in the estimate of the mean by a factor of 2.1.
When fewer potential predictors are retained after the first step of the bootstrap model selection procedure, this variability seemed to decrease and model selection frequency increased.
If we were to modify our model to incorporate this variability along with stronger bottlenecks or smaller population sizes within the Americas relative to those in non-American populations, then we might be able to produce patterns that agree with the observed data.
Random intercepts were used to model the variability between patients.
Random fields have been used to model the variability of the material properties.
Animal models greatly reduce this variability and heterogeneity, and provide a means to study mTBI in a rigorous, controlled, and efficient manner.
For mathematical models to capture this variability, a stochastic description is required.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com