What I keep coming to here is, doesn’t the entire point of this post come to the situations where the parameters in question, the bias of the coins, are not independent? And doesn’t this contradict?
estimate 100 independent unknown parameters
Which leads me to read the later half of this post as, we can (in principle, perhaps not computably) estimate 1 complex parameter with 100 data sets better than 100 independent unknown parameters from individual data sets. This shouldn’t be surprising. I certainly don’t find it as such.
The first half just points out that in the independent case of this particular example, Bayesian and Frequentist perform equivalently for relatively similar assumptions. But cousin_it made a general claim about the Frequentist approach, so this isn’t worth much weight on its own.
What I keep coming to here is, doesn’t the entire point of this post come to the situations where the parameters in question, the bias of the coins, are not independent? And doesn’t this contradict?
Which leads me to read the later half of this post as, we can (in principle, perhaps not computably) estimate 1 complex parameter with 100 data sets better than 100 independent unknown parameters from individual data sets. This shouldn’t be surprising. I certainly don’t find it as such.
The first half just points out that in the independent case of this particular example, Bayesian and Frequentist perform equivalently for relatively similar assumptions. But cousin_it made a general claim about the Frequentist approach, so this isn’t worth much weight on its own.