Confidence interval interpretation difficulty I have seen a lot of questions in this forum related to what my question is, but I didn't find any convincing answer. So I would to like to put this question:
When we are dealing with 95% confidence interval we mean that if we repeat process of collecting samples of same size and calculate 95% intervals for those samples then 95% of those intervals will contain the true population parameter.
Let the infinite number of intervals be represented by 100 for simplicity. Then 95 of these intervals will contain true population parameter.
Suppose we got an interval at the starting of the above process (L,U). 
Then if I ask what is the probability that this interval (L,U) contains the true population parameter then shouldn't it be 95/100 = 0.95? (Because this interval (L,U) can be anyone of 100 and it would contain true population parameter of its one of those 95). 
But this interpretation of confidence interval is considered incorrect. Can someone explain me why is this so?

In a frequentist approach, that is the case of the Classical Statistics Confidence Intervals, they may strictly be interpreted only in a relation to a sequence of similar inferences that might be made in repeated practice.
On the contrary, in Bayesian thinking this argument facilitates the common sense interpretation of statistical conclusion. For instance, a Bayesian Credible Interval for an unknown quantity of interest can be directly regarded as having high probability of containing the unknown quantity.
Similar comment applies to the comparison of Bayesian and frequentist approaches to hypothesis testing.
