Err, actually, yes it is. The frequentist interpretation of probability makes the claim that probability theory can only be used in situations involving large numbers of repeatable trials, or selection from a large population.
It is argued that the proposed frequentist interpretation, not only achieves this objective, but contrary to the conventional wisdom, the charges of ‘circularity’, its inability to assign probabilities to ‘single events’, and its reliance on ‘random samples’ are shown to be unfounded.
and
The error statistical perspective identifies the probability of an event A—viewed in the context of a statistical model Mθ(x), x∈R^n_X—with the limit of its relative frequency of occurrence by invoking the SLLN. This frequentist interpretation is defended against the charges of [i] ‘circularity’ and [ii] inability to assign ‘single event’ probabilities, by showing that in model-based induction the defining characteristic of the long-run metaphor is neither its temporal nor its physical dimension, but its repeatability (in principle) which renders it operational in practice.
Depends which frequentist you ask. From Aris Spanos’s “A frequentist interpretation of probability for model-based inductive inference”:
For those who can’t access that through the paywall (I can), his presentation slides for it are here. I would hate to have been in the audience for the presentation, but the upside of that is that they pretty much make sense on their own, being just a compressed version of the paper.
I am not enough of a statistician to make any quick assessment of these, but they look like useful reading for anyone thinking about the foundations of uncertain inference.
Depends which frequentist you ask. From Aris Spanos’s “A frequentist interpretation of probability for model-based inductive inference”:
and
For those who can’t access that through the paywall (I can), his presentation slides for it are here. I would hate to have been in the audience for the presentation, but the upside of that is that they pretty much make sense on their own, being just a compressed version of the paper.
While looking for those, I also found “Frequentists in Exile”, which is Deborah Mayo’s frequentist statistics blog.
I am not enough of a statistician to make any quick assessment of these, but they look like useful reading for anyone thinking about the foundations of uncertain inference.