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Although initially developed for sediment samples, the model is not limited in its application; it can also be used to approximate any other multimodal continuous distribution function.
However, for less dense samples, the model accounted for R-squared (R2) of 97.5% and 99.3% in the open cavity and closed cavity resonators, respectively.
At the high end of 1000 samples, the model explained 96.4 ± 0.3% of the variance.
We saw that with 4 weekly sampling (3 samples) the model was very sensitive to observations taken on day 28.
For each group of 200 bootstrap samples, the model was refitted and tested against the observed sample in order to derive an estimate of the predictive accuracy and bias.
After learning from a "training set" of biological samples, the model should be able to correctly classify new samples exposed to compounds that the SVM has not encountered before.
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With short-term data samples, the models to be investigated can be parameterized and their predictions be compared.
After obtaining the posterior parameter samples, the models were tested against each other using Bayes factors and the Deviance Information Criteria (DIC) [ 30].
Bagging [8] can be seen as a method for sampling the model space.
This involves adaptively sampling the model parameter space using an algorithm, which biases the sampling towards regions of good fit.
During its operation the process was sampled, the model was updated and the optimization procedure was applied.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com