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Multilevel modeling, using multiplicative model structure allows interpreting training effects over time in aerobic fitness, considering the complexities accounted to the influence of size and maturation during adolescence.
Further the model structure allows the separate analysis of all implemented cost contributions.
The micro-scale model structure allows PEP behaviors which appear to be distinct to be represented within a unified setting.
However, the model structure allows for incorporating EVLA into the analysis and can be adapted to other settings.
The average of 10 random samples from each copula was used in this study; however, the model structure allows users to change this value.
The decoupled multiple model structure allows obtaining a dynamical model for the soft sensor despite the presence of practical constraints related to multi-rate sampling problem.
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Our model structure allowed us to partition the contribution of different transmission routes in sustaining the epidemic.
Our novel model structure allowed us to partition the contribution of different transmission routes in sustaining the epidemic.
However, Bayesian, computer-intensive methods place few restrictions on model structure, allowing us to apply a negative binomial sampling distribution to estimate the over-dispersion in the data and thus quantify parameter uncertainty rigorously and comprehensively for the multi-species case.
Our model structure allowed us to observe aspects of growth and migration hitherto not reported in other systems.
Using a linear model structure allowed for comparability across the different models in this paper, and with previous literature (e.g., Cummings et al. 2006).
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