Ed Bein writes:
I’m hoping you can clarify a Bayesian “metaphysics” question for me. Let me note I have limited experience with Bayesian statistics.
In frequentist statistics, probability has to do with what happens in the long run. For example, a p value is defined in terms of what happens if, from now till eternity, we repeatedly draw random samples from some population of interest, compute the value of a test statistic, and keep a running tabulation of the proportion of values that exceed a certain given value. Let me refer to probability in a frequentist context as F-probability.
In Bayesian statistics, probability has to do with degree of belief. Prior and posterior distributions refer to our degree of confidence (prior to looking at data and after looking at data, respectively) that a parameter falls within certain ranges of values, where 1 represents total certainty and 0 represents total disbelief. Let me refer to probability in a Bayesian context as B-probability.
Both F-probability and B-probability are valid interpretations of probability, in that they satisfy the axioms of probability. But they are distinct interpretations.
My conceptual confusion is that Bayes Theorem combines a term with an F-probability interpretation (the likelihood, which is essentially the density of the sampling distribution) with a term with a B-probability interpretation (density of the prior distribution) to produce an entity with a B-probability interpretation, namely, the density of the posterior distribution. I’m not questioning the validity of the derivation of Bayes Theorem here. Rather, it seems conceptually messy to me that an F-probability term is combined with a B-probability term; both terms have to do with “probability,” but what is meant by “probability” is very different for each of them.
Can you provide some conceptual clarity?
At this point, I’ve written about this so many times I just have to point to the relevant links. Kinda like that joke about the jokes with the numbers.