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The probabilistic satisfiability (PSAT) problem is to determine whether it is possible to find a probability measure over truth assignments such that all assessments are satisfied [13].
The probabilistic satisfiability (PSAT) problem is to determine whether a given set of probabilistic assessments (a PSAT instance) can be satisfied in the sense that there is a probability measure over truth assignments such that all assessments are satisfied.
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Furthermore, we have a probability measure over q+1 truth assignments, represented by the real-valued variables p 1,…,p q+1∈[0,1], that must sum up to 1.
To understand our goal, note that the satisfiability of probabilistic formulas P(ϕ i )=α i, 1≤i≤q obtains when there is a probability measure (over the truth assignments) that satisfies Pleft phi_{1}right) = alpha_{1} wedge Pleft phi_{2}right) = alpha_{2} wedge dots wedge Pleft phi_{q}right) = alpha_{q},.
To establish semantics for these assessments, we consider a probability measure over the set of truth assignments.
There is a measure of truth here.
Sampling error is not a measure of truth or accuracy.
Neither assertion is without a measure of truth.
To be sure, the act reflects a measure of truth.
There was a measure of truth in that.
As generalities they do contain a large measure of truth.
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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