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To determine each model parameter, the available literature was surveyed to estimate the model parameters.
Sensitivity is a measure of the amount of change in the MT impedances caused by variations in a model parameter (the log of the electrical resistivity).
This constrained control objective aims to maximize the sensitivity of the model output with respect to the unknown model parameter (the time constant).
A key model parameter, the Darcy permeability of the bone lacunar canalicular system (LCS), is difficult to measure using traditional methods due to the co-existence of the larger vascular and smaller LCS porosities.
Given that previous attempts in the literature have neither include this kind of patterns nor the time as a crucial model parameter, the present model offers a suitable alternative for the computational simulation of this paradigm.
Also, as previous observed [42], we found that the prediction accuracy did not change much as the remaining model parameter, the number of variables per tree, was varied.
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To estimate the model parameters, the stochastic gradient descent procedure is used.
Under the same set of model parameters, the levels are averaged over multiple trajectories.
For the 26 calibrated model parameters, the four Markov chains converged within 20,000 iterations.
We used 100,000 iterations to estimate model parameters; the first 10,000 iterations were discarded.
This result is not surprising: the more data used to estimate model parameters, the better the classifier performs.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com