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Nonetheless, the assertion of the empirical data D is the only empirical element in the probability judgement, which otherwise has no testable implications for experience.
So, in AKV, Lewis (1946, 305) claimed that the full statement of a probability judgement should be of the form "That c, having property F, will also have property G, is credible on data D, with expectation a/b and reliability R", and is assertable in whatever sense D is.
There were no group differences in probability judgement.
Probability judgement did not differ significantly between the lesion groups and the healthy comparison group.
The subject is required to make a probability judgement (which colour hides a concealed token) followed by a wager.
Similar(55)
There's another problem as well, which arises because making probability judgements after the whole thing has happened is, in essence, dodgy.
Second, he argued that, if probability judgements were empirical frequency claims, then the probability judgements would themselves only be probable, something that can't be coherently accounted for.
The black box outputs "discernments": qualitative probability judgements like "$X$ is more likely than $Y$".
Haenni et al. (2011) motivate imprecise probabilities by showing how they can arise from precise probability judgements.
Of course, if we go beyond this to more complex (and admittedly more interesting) statements, for example, to hypotheses of ancestry and descent, the picture becomes murkier because such notions cannot be tested; we are back to probability judgements, and unquantifiable ones at that, although now we can see where they are coming from.
We make no further probability judgements on the values of each of the active inputs.
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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