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Pale, single-values distributions correspond to parameter values fixed within the model (κ 6 = 0 to prevent mtDNA proliferation after development; τ 1 = 7 hr, τ 2 = 16 hr fixed by data on cell doubling times; h 0 = 0.2 fixed for simplicity as heteroplasmy variances are normalised; δλ = 0 fixed to avoid varying selective pressure).
In this paper, we empirically evaluate these tradeoffs with special attention to parameter values that are relevant for developing economies.
Uncertainty in the model predictions and sensitivity to parameter values was assessed by probabilistic modelling using Monte Carlo methods.
Simulated field data are matched by a four-parameter theoretical model and sensitivity of results to parameter values is investigated.
This simple example illustrates a wide variety of complicated dynamics and shows that the qualitative dynamic features of isothermal reaction systems may be extremely sensitive to parameter values.
Good control results are strongly related to parameter values used by observers or estimators, which vary according to machine working conditions and temperature.
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Additionally, parameter linking can be used to fix parameter values to each other.
Monte Carlo Simulation serves to estimate parameter values and to assess uncertainty for three calibration cases.
The fitness of such populations seems to be highly sensitive to dispersal parameter values.
The lower half of our 68% probability interval relates to input parameter values that are close to LIg values (see above).
This relatively low accuracy is in most cases due to our deliberate decision not to tweak parameter values arbitrarily.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com