Downvoted for an uncharitable interpretation of the OP.
You seem to have cherry-picked examples where collecting additional evidence appears to be either very costly or impossible. And failed at it. In nearly all your examples it is possible and advisable to collect more evidence before (or while) going all Bayesian on the problem (the first half of the OP’s point). And with enough evidence there would be little difference between Bayesian and frequentist calculations. Which is the other half of the OP’s point. You missed both halves.
Based on Nyan’s subsequent post, what he was trying to say was: “get more data,” which is a point, as he correctly points out (not in the OP though) that is orthogonal to B vs F.
Ok. I guess I was confused by this start:
“Bayesian epistemology and decision theory provide a rigorous foundation for dealing with mixed or ambiguous evidence, uncertainty, and risky decisions. You can’t always get the epistemic conditions that classical techniques like logic or maximum liklihood require, so this is seriously valuable.”
Also by the fact that the word “Bayesian” is used lots of times in the OP. I like causal graphs. It doesn’t mean I have to sprinkle them into every post I make on every subject :).
I was not trying to cherry pick examples for any particular purpose. These examples were all difficult decisions I had to make in my life, except the last one, which is an academic example where B vs F considerations are very subtle. I don’t know what it means to “go Bayesian” on these examples. What made them difficult was not the kind of thing that Bayes theorem would have made easier.
I guess my view is, unless you are doing stats/machine learning (and maybe not even then!), you ought to have no opinion on B vs F. This is an argument that will not affect your life.
I guess my view is, unless you are doing stats/machine learning (and maybe not even then!), you ought to have no opinion on B vs F. This is an argument that will not affect your life.
Huh, I thought your examples (some of which are life-affecting) are supposed to demonstrate that there are plenty of cases where for the same limited set of data B > F, but it looks like I misunderstood your point completely. Sorry about that.
Ok. Some typical examples of risky decisions under uncertainty people have to make:
(a) Should I take a job in a new city?
(b) Should I go to graduate school or get a job out of college?
(c) Should I buy a house now (e.g. what will the housing market do in 5 years?)
(d) Should I marry this person?
Here is another problem that is more academic:
(e) How do I learn a causal graph from data?
Bayesian epistemology (or approximation thereof) vs frequentism, go! I am calling your bluff. Do you actually know what you are talking about?
Downvoted for an uncharitable interpretation of the OP.
You seem to have cherry-picked examples where collecting additional evidence appears to be either very costly or impossible. And failed at it. In nearly all your examples it is possible and advisable to collect more evidence before (or while) going all Bayesian on the problem (the first half of the OP’s point). And with enough evidence there would be little difference between Bayesian and frequentist calculations. Which is the other half of the OP’s point. You missed both halves.
Based on Nyan’s subsequent post, what he was trying to say was: “get more data,” which is a point, as he correctly points out (not in the OP though) that is orthogonal to B vs F.
Ok. I guess I was confused by this start:
“Bayesian epistemology and decision theory provide a rigorous foundation for dealing with mixed or ambiguous evidence, uncertainty, and risky decisions. You can’t always get the epistemic conditions that classical techniques like logic or maximum liklihood require, so this is seriously valuable.”
Also by the fact that the word “Bayesian” is used lots of times in the OP. I like causal graphs. It doesn’t mean I have to sprinkle them into every post I make on every subject :).
I was not trying to cherry pick examples for any particular purpose. These examples were all difficult decisions I had to make in my life, except the last one, which is an academic example where B vs F considerations are very subtle. I don’t know what it means to “go Bayesian” on these examples. What made them difficult was not the kind of thing that Bayes theorem would have made easier.
I guess my view is, unless you are doing stats/machine learning (and maybe not even then!), you ought to have no opinion on B vs F. This is an argument that will not affect your life.
Huh, I thought your examples (some of which are life-affecting) are supposed to demonstrate that there are plenty of cases where for the same limited set of data B > F, but it looks like I misunderstood your point completely. Sorry about that.