Here’s the experiment and here’s the data is concrete it may be bogus but it’s information. Updating probabilistic based on recursive estimates of probabilities is largely restating your assumptions. Black swans can really throw a wrench into things.
Plenty of downvotes and comments, but nothing addressing the point of the argument might suggest something.
This is called modelling error. Both Bayesian and frequentist approaches suffer from modelling error. That's what TFA talks about when mentioning the normality assumptions behind the paper's GLM. Moreover, if errors are additive, certain distributions combine together easily algebraically meaning it's easy to "marginalize" over them as a single error term. In most GLMs, there's a normally distributed error term meant to marginalize over multiple i.i.d normally distributed error terms.
> Plenty of downvotes and comments, but nothing addressing the point of the argument might suggest something.
I don't understand the point of your argument. Please clarify it.
> Here’s the experiment and here’s the data is concrete it may be bogus but it’s information. Updating probabilistic based on recursive estimates of probabilities is largely restating your assumptions.
What does this mean, concretely? Run me through an example of the problem you're bringing up. Are you saying that posterior-predictive distributions are "bogus" because they're based on prior distributions? Why? They're just based on the application of Bayes Law.
> Black swans can really throw a wrench into things
A "black swan" as Taleb states is a tail event, and this sort of analysis is definitely performed (see: https://en.wikipedia.org/wiki/Extreme_value_theory). In the case of Bayesian stats, you're specifically calculating the entire posterior distribution of the data. Tail events are visible in the tails of the posterior predictive distribution (and thus calculable) and should be able to tell you what the consequences are for a misprediction.
My point is this: You can’t combine them using Bayesian statistics adjusting for the possibility of research fraud it’s simply not in the data.
Their great for well understood domains, less so for research. Frequentist models don’t work, but they also don’t even try.
PS: Math errors don’t really fall into modeling error.
In re, the arguey-person you were responding to, frequentist modeling is just as bad or worse for these sorts of situations.
Your investigation isn’t limited to the data provided by them it’s going to look for more information beyond the paper. This isn’t a failure of frequentist models because they evaluate the study and it’s output separately.
If you use a model that doesn't ask you to think about this likelihood at all, you will get the same result as if you had used bayes and consciously chose to approximate the likelihood of misclassification as zero.
You may get slightly better results if you have a reasonnable estimate of that probability, but you will get no worse if you just tell Bayes zero.
It feels like you're criticizing the model for asking hard questions.
I feel like explicitely not knowing an answer is always a small step ahead of not considering the question.
As much as people complain about frequentist approaches, examining the experiment independently from the output of the experiment effectively limits contamination.
Now what’s the count, wait what’s the likelihood it misclassified a ball? How accurate are those estimates, and those estimates of those ...
For a real world example someone using Bayesian reasoning when counting cards should consider the possibility that the deck doesn’t have the correct cards. And the possibility that the decks cards have been changed over the course of the game.