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Alternatively, similarity/difference between a model and reference structure can be expressed in the form of superposition-free measures such as the local Distance Difference Test (lDDT) score (Mariani et al., 2013) assessing the differences in interatomic distances between model and reference structure.
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Simulations are carried out to show the good agreement between reduced model and reference model.
To tune proxy models, we propose a parameter-estimation scheme that incorporates prefiltering of prediction errors between the proxy model and reference data.
Moreover, the absence of clear demarcation between model and non model with reference to distance (closeness of model and non model) may have created information contamination as well as diarrheal disease transmission to the model HH members and vice versa.
The objective function is based on the difference between outputs of the predictive model and reference trajectory.
It can be divided into two parts: the first item of Eq. (7) is the smallest model between recovered and reference model, and the last three items are the smoothest model between recovered and reference model in longitudinal, latitudinal, and radial directions, respectively.
The similarity/difference between a model and a reference structure can be expressed in the form of distances between corresponding atoms in the model and its reference structure after performing a global superposition.
To establish a comparison between our model and a reference model that do not use population information, at least, we must focus on the same performance metric, i.e., the expected number of identifications (which can also be viewed as throughput).
For model fit, we report Log Bayes Factors (LBF) – the difference in AIC between each model and a reference model.
We report log evidence differences or log Bayes Factors (LBF) – the difference in log likelihood between each model and a reference model, for each step in model comparison.
The association between depressive symptoms and incident diabetes remained significant, even after adjusting for all covariates (HR 1.62, 95% CI 1.15 2.29) (16% reduction in the HR between this model and the reference model) (Table 2).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com