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Frequentist here. This paper makes me hate Bayesians a little less. The reason is because a general thrust of the paper (since I have only had time to give it

by olympus 14y ago

Frequentist here. This paper makes me hate Bayesians a little less. The reason is because a general thrust of the paper (since I have only had time to give it a once-over) seems to be that just because you are a Bayesian it doesn't mean that you have to get rid of model adequacy checks. Not having model adequacy checks is why I think Bayesians run around with a magic wand saying, "poof! there's an optimal model." After proving a theoretical optimality they never check to see if the real world data supports their arguments. So I'm glad to see a prominent Bayesian saying that you don't have to throw model checking out the window. On a secondary note, I have to lament the use of philosopy in a math paper. I realize that many prominent mathematicians are/were also philosophers and that the two subjects are somehow linked at some level. But really I think that putting philosophy in a math paper is an excuse to use more big words and sound smart. Most of us would like to have a set of formulas to apply and not worry about-- forgive me if I for what I'm about to say-- fuzzy non-science like philosophy and the implications that it might have on our cold hard numbers. Can each 31 page paper that combines math with philosophy come with a 5 page companion paper that leaves out the philosopy and just has the applicable math stuff?

rafcavallaro 14y ago
I think your objection misses the fundamental point of the paper which is that blindly applying formulae (in this case Bayesian ones) without considering the part these computations play in the whole scientific process leads to bad science. Specifically, it leads to assuming that the correct model is already among those being considered. The authors say this is often not the case and assuming so often leads to stagnation in the relevant discipline. This isn't an article that hands the readers a set of rote instructions, it's a warning that rote application is bad science because good science doesn't merely compare existing models with the data, good science proposes new and better models. This is fundamentally an article on the philosophy of science so leaving out the philosophy would make the article pointless. In general, people who are good at symbolic manipulation are often in search of a methodology that allows them to only do symbolic manipulation and relieves them of the difficult task of doing semantics as well. The article is saying that there's no free lunch here - we have to do the semantics as well, we have to understand why models don't perform well and come up with the creative insights necessary to replace them with new ones.

by olympus 14y ago

rafcavallaro 14y ago

I think your objection misses the fundamental point of the paper which is that blindly applying formulae (in this case Bayesian ones) without considering the part these computations play in the whole scientific process leads to bad science. Specifically, it leads to assuming that the correct model is already among those being considered. The authors say this is often not the case and assuming so often leads to stagnation in the relevant discipline. This isn't an article that hands the readers a set of rote instructions, it's a warning that rote application is bad science because good science doesn't merely compare existing models with the data, good science proposes new and better models. This is fundamentally an article on the philosophy of science so leaving out the philosophy would make the article pointless. In general, people who are good at symbolic manipulation are often in search of a methodology that allows them to only do symbolic manipulation and relieves them of the difficult task of doing semantics as well. The article is saying that there's no free lunch here - we have to do the semantics as well, we have to understand why models don't perform well and come up with the creative insights necessary to replace them with new ones.