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It is possible to (1) describe how pseudo-replication in sampling influences the overall variability in response data using mixed-effects modeling, (2) integrate data to explicitly model the sampling process and account for bias using a hierarchical modeling framework, and (3) examine the relative influence of many different or related explanatory factors using machine learning tools.
Simulations confirm the results of analytical investigations and they allow us to model the sampling method of the signals of the angular velocity and the phase angle between the rotors.
Three mixture models were exploited for their potentials to model the sampling frequencies.
Kuznestsov and colleagues [22] extended discrete Pareto-like distribution to model the sampling frequencies directly, but gave no implication on the distribution of true relative abundances.
If we can describe the data generation process by a parametric model, the sampling distribution can often be derived analytically, at least to a good degree of approximation.
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That is, given the generalized prior model, the samples needed by GPR model could be reduced greatly meanwhile keeping precise.
In the second stage SVM model, the sample is set as (x i, y i ), i = 1, 2, ⋯, l, x i ∈ R n.
To obtain solutions from the proposed model, the sample average approximation (SAA) method is used to obtain statistically certified solutions from a reduced number of scenarios.
For second-part model, the sample size varies across measures depending on the number users for the specific services.
This means that, under the additive model, the sample may show little or no deviation from HWE.
The loss function is a function of parameter values of the model (the sample partition, and the component-specific parameters), and an action.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com