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In this characterization of Bayesian statistical inference the probability of the data $P s)$ is not presupposed, because it can be computed from the prior and the likelihoods by the law of total probability, \[ P s) \; = \; \sum_{\theta \in \Theta} P(h_{\theta}) P(s \mid h_{\theta}).
To characterize how pollen tubes approached ovules, we limited the angles in this characterization to cos θ mp ≥ 0. We use these statistics to summarize how different incubation times affected the deviation in guidance represented by the Δ θ angle for pollen tubes with tips 0-50 μm and 50-100 μm from a micropyle (Fig. 2D).
Skeptics can take an almost unseemly pleasure in this characterization.
But Mr. Borrelli takes such joy in this characterization and invests it with such absolute-monarch confidence that there is nothing remotely offensive about it.
Numerical models are a helpful tool in this characterization process.
However, morally important distinctions e.g., between differences in character—have no place in this characterization.
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"[Y]ou have succeeded to put all of us Messiah students in a box, when in reality this characterization couldn't be further from the truth.
In part, this characterization is discouraging because in fact we know a great deal about how new species form.
In addition, this characterization could indicate additional roles of OPNc in different aspects of tumor progression.
(In fact, this characterization is inaccurate: most major rappers disdain white gold, considering it inferior to platinum).
Unfortunately, phenomenology is often mistaken to be synonymous with experience.2 Even if there is a weak sense in which this characterization is correct, it is nevertheless inaccurate.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com