[kgwgk]

> It is not a distribution over the variable of interest any more than a frequentist's ensembles are.

It can be right or wrong but it is what it is!

The frequentist ensembles in the frequentist inference are absolutely not a distribution over the variable of interest. They are defined for a fixed value of the variable of interest.

The 95% confidence interval means that 95% of the intervals that you generate in the ensemble where the paramater has always whatever its unknown actual value is will include it.

[whatshisface]

Wouldn't that imply that every Bayesian calculation ever done was wrong? The chances that you'd choose the right ultimate prior function R->R from that set are measure zero.

[kgwgk]

The calculation Input -> [Model] -> Output is not wrong.

I don't know what are you trying to say but it's no longer related to whether Bayesian methods and frequentist methods give the same answers. They don't even try to represent the same questions.

PS: I didn't have time to reply to your later comment, so here is a final comment reaching for common ground.

> frequentist conceptual machinery

Just to be clear, that's not what I've been talking about. As I'm tried to make clear several times I'm talking about what is commonly understood as frequentist methods. I gave the concrete example of confidence intervals. I started the conversation with a explicit question: What do you call "frequentist methods"?

I agree that if we define "frequentist conceptual machinery" to be "probability" we can do Bayes with it.

[whatshisface]

Frequentism and bayesianism are more than separate collections of formulas, they're philosophical stances on interpreting theorems about statistics. That's where the separate language comes from. It might not be taught very often (because it's inconvenient and wordy), but it is actually possible to address anything in frequentist language, even cases where you're combining separate sources of differently-weighted evidence. The same goes for bayesian language.