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The balance between model precision and parsimoniousness was assessed using Akaike's information criterion (AIC) [ 29].
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There is no significant difference between model precision (R: t-test, P = 0.310) or accuracy (MSE: t-test, P = 0.324) when excluding siRNAs from the training set that overlap with any of those in the testing set.
However, loss in model precision between the 6th and the 45th month was small for most compounds.
This stability was also apparent in the very low variation in model precision between simulations with fixed and varying coefficients.
Model precision is based on the ability to fit a relationship between predicted and empirically observed activities (namely the Pearson correlation or R fit of the model between predicted and observed).
Above, we introduced 3 perceptual models and 3 response models ("precision", "belief", and "surprise").
This method enables a modeller to incorporate the information obtained from the experimental data in the assessment of the uncertain model predictions and to find a balance between model performance and data precision.
Within-run precision, between-run precision and bias were all ≤14.3%.
Intermediate precision therefore represents 'within-laboratory, between-run precision' and is therefore a useful measure for inclusion in ongoing validation.
We also compared the accuracy and precision in the demographic estimates between model averaging and when the true model was known.
The comparison illustrates a good correlation between model prediction and test results, and a clearly improved performance in terms of accuracy and precision is achieved.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com