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Methodologies, like co-simulation which allows for parallel simulation at different time-scales in different modules, and modeling environments for integrative models including different types of equations (ODEs, PDEs, SDEs etc) were discussed and evaluated.
In general the differences in accuracy of prediction between models including different number of SNP are small, and are small compared to their standard error.
Comparisons of the different mixture models, including different traits, through their ability to predict growth, showed small and inconsistent differences between the models.
We compare models including different objectives or constraints in this paper, as shown in Table 1.
We compared DIC of models including different effects.
Models including different regimes are still difficult to perform.
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These models include different combinations of risk factors.
Capitation models include different financial incentives for physicians (such as incentives for preventive care and chronic disease management) and they also include additional funding for interdisciplinary care.
We used IDI as a continuous measure of reclassification, calculated by subtracting the mean difference of predicted risk between the clinical model (16) and the model including different biomarkers.
Using a newly released data set we estimate a multinomial logit duration model including different measures capturing the economic incentives embedded in the social security system.
Monte Carlo experiments are performed to compare these algorithms across different specifications of the model including different marginal distributions and degrees of persistence for the instantaneous variance.
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