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With regard to the fluorescence, similar trends were observed when comparing the TMV samples; the underlying photophysics however differed.
For datasets with high-intrinsic dimensionality and low number of samples, the underlying estimation of the manifold might be difficult and highly variable.
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Another class of methods are the so-called Monte Carlo (MC) methods in which the probability space is sampled, the underlying deterministic PDE is solved for each sample and the samples are combined to determine statistical information about the random field.
Finally, it is noted that while there are some changes in the estimated coefficient values between the two models presented in Table 2 (base one and one where the observations have been weighted to account for the underrepresentation of male respondents in the sample), the underlying trends still apply.
We pose the problem as estimating the parameters of a Gaussian mixture model (GMM) from samples of the underlying distribution, where the mixture weights are equal and set to 1/M.
Major strengths of the study are: representative large samples from the underlying general population, and use of official register data.
Since we are concerned with the generation of plausible datasets, rather than just plausible samples of the underlying function values, it follows that we are actually interested in y(x) rather than f(x).
First, our study children were identified through a convenience sample rather than a representative sample of the underlying population.
These analyses used methods developed for case-cohort samples that assume the controls in this study approximate a hypothetical sample of the underlying "cohort" that gave rise to the cases.
In this paper, we address this heterogeneity problem by stratifying the reservoir sample among the underlying sub-streams.
This is a common situation in model-based sampling since the underlying population model is usually unknown.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com