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We used diagnosis as a fixed factor to compare migraineurs in the interictal phase and controls.
Besides the MAPE criterion, consistency of results is another vital factor to compare prediction approaches.
That will give consumers another factor to compare machines by, other than weight, size and processor speed.
Therefore, the ration of ID/IG proves to be an effective Raman factor to compare the thicknesses of plasma-treated few-layer graphene sheets.
By Bayes' theorem we have \[ \frac{P(h_{\theta} \mid s)}{P(h_{\theta'} \mid s)} \; = \; \frac{P(h_{\theta}) P(s \mid h_{\theta})}{P(h_{\theta'}) P(s \mid h_{\theta'})}, \] and if we assume equal priors $P(h_{\theta}) = P(h_{\theta'})$, we can use the ratio of the likelihoods of the hypotheses, the so-called Bayes factor, to compare the hypotheses.
We used a generalized linear mixed effects model, with 'nest' as a random factor to compare the information available to carers at mid- and late-nestling phases.
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Then, we use different LOS, arrival rates and peak-hour factors to compare our model with the existing design models.
In addition, as well as the conventional analyses described above, we used Bayes factors to compare the null and alternative hypotheses (Dienes, 2014).
Truth: When shopping around for a mortgage, interest rates are one of the most important factors to compare.
To investigate which partitioning model is preferred, we calculated Bayes factors to compare pairs of likelihood models.
Using graphical evidence and Bayes factors to compare each two-piece exponential to the Weibull, we find evidence in favor of the two-piece models.
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