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All these factors ensure the validity of the assumption about equal probability of models in Equation 16.
The model in Equation (3) reduces to the form of the ODE models in Equation (1) if vanishes.
Using the model response PDFs from Equation (5) for two competing models, in Equation (9) provides us with a measure of average model dissimilarities.
The transition function for synchronous models given in Equation (3) is modified to Equation (12) and asynchronous models in Equation (5) is modified to Equation (13).
In this study, two formulations of the parameter estimation of ODE models in Equation (1) are investigated, involving the minimization of concentration and slope errors.
From the network point-of-view, the interspecies PPI network depicted by the multivariate linear dynamic models in equation (1) represents how C. albicans and zebrafish interact during the infection process.
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We provide a unified multiscale framework for including such models in equation-oriented process flowsheet modeling, simulation and optimization.
For the overlapping region, we calculated the transition-probability matrixes (based on the overlapping models in equations 5 and 6).
The models in Equations 2 and 3 can then be combined to form a single surface (Figure 5) that captures the stimulus-response relationship in terms of bits of uncertainty.
Each version of our statistical models in Equations (1 3) generates a likelihood under the cn = 1, 2, 3 scenarios.
When this is the case, we no longer expect that the residuals for the models in Equations (4) and (5) will be independent.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com