The phrase "a precise probability" is correct and usable in written English.
It can be used when discussing a specific likelihood or chance of an event occurring, often in mathematical or statistical contexts.
Example: "The study provided a precise probability of success for the new treatment, which was calculated based on extensive clinical trials."
Alternatives: "an exact likelihood" or "a specific chance".
"There's no way to come up with a precise probability of terrorism, because there are too many variables, but it's fortunately quite low".
That is, if you have a precise probability for $X$ and a precise probability for $Y$, then you can put bounds on $p(X\cap Y)$ and $p(X \cup Y)$, even if you don't know how $X$ and $Y$ are related.
It is a consequence of the rules for obtaining the state-space of the composite system that the combined state of the pair is 1/√2|A=a1>s|B=ground state>s* + 1/√2|A=a2>s|B=ground state>s* Indeed, it entails that there is a precise probability of 1/2 that it will end up in the former, and a probability of 1/2 that it will end up in the latter.
Williamson (2014) makes this point, and argues that a single precise probability is the correct level at which to get off the "uncertainty escalator".
That is, the MaxEnt approach makes sense if you are already committed to representation of belief by a single precise probability, but loses its appeal if credal sets are available.
As mentioned at the outset, we might represent an agent by the set of propositions that they believe; or we might represent them using a set of precise probability functions; or a comparative confidence ordering; or a precise primitive conditional probability function.
The idea is that inside the black box there is a numerical and precise probability function that you only have indirect and imperfect access to (Good 1962).
It is not possible to calculate the precise probability that a person living in a certain location moved, as some of the people previously living in that location will not have filled out a census form 5 years later for various reasons.
In our experiment, the evidence is determined by the Log numerical distance and the precise probability of generating a response given a stimulus can be estimated using bayesian models based on maximum likelihood hypothesis [55], [56].
Typically P represents data, observations, or a precise calculated probability distribution.
Evidence theory has strong ability to handle epistemic uncertainties whose precise probability distributions cannot be obtained due to limited information.
