IIRC, in bayesian terms, confidence intervals express `P(X ∈ [A,B] | X=x) = 0.95`, where X is the unknown parameter and [A,B] is the interval, assumed to be som

IIRC, in bayesian terms, confidence intervals express `P(X ∈ [A,B] | X=x) = 0.95`, where X is the unknown parameter and [A,B] is the interval, assumed to be some fixed function of the data. Ordinarily I think they expect this to be satisfied for all values x. So this is P = 95% where the parameter is known but the interval is not (because the interval depends on the data, which is not known yet). On the other hand credible intervals express `P(X ∈ [A, B] | A=a,B=b) = 0.95` (or more generally `P(X ∈ [a,b] | the data) = 0.95`). The latter is what is intuitively meant by "95% probability" of the true parameter being in the interval, because you do know a and b but not the parameter. The example with random sampling of confidence intervals from {ℝ⁺, ℝ⁻} is indeed a good illustration of the difference.