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How should decision theory interpret the probability of a state S if one performs an act A, that is, P(S if A)? Probability theory offers a handy suggestion.
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To make expected utility track efficacy rather than auspiciousness, causal decision theory interprets the probability of a state if one performs an act as a type of causal probability rather than as a standard conditional probability.
Interpreting the probability of disease given a certain exposure (the ESR) is much more transparent.
For the moment, assume that we can interpret the probability over hypotheses as an expression of epistemic uncertainty.
On the other hand, it does interpret the probabilities over sample space as components of a support relation, and thereby as pertaining to the epistemic rather than the physical realm.
The likelihood of reflection then can be interpreted as the probability of relapse.
It has rather to be interpreted as the probability of being eligible.
This can be interpreted as the probability of bankruptcy for miner i.
Thus, the ratio S/T can be interpreted as the probability of a given set δ of erased digits to be a stopping set.
Landslide susceptibility also follows a Bernoulli distribution and the results may be interpreted as the probability of occurrence, in static conditions, of a landslide.
Alternatively, the baseline hazard can be interpreted as the probability of an event occurring if the explanatory variables are set equal to zero.
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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