ulfgard

But getting back into a well-defined state which you can communicate via an error is a viable recovery strategy.

This state can be "the database is corrupt", allowing for a restore from backup, it can also be "shut down gracefully" and communicate that.

Especially for databases there is a ton to concinder: what about database entries that are not committed yet? When you run into oom, should you at least try to commit the data still in ram by freeing as much space as possible?

Depending on the hypothesis space what you call the "uninformative prior" does not exist in the frequentist approach. If you search for a real value, then the uninformative prior is a uniform distribution on the infinite line. This distribution does not normalize.

Ultimately, I think you are strawmanning frequentism here. Just because the log likelihood is sometimes the same as the map does not imply that they have the same meaning. This is why computed uncertainties of both approaches are often not the same and have a not-so-subtle difference in their interpretation. The one computes uncertainty in belief, the other imprecision of an experiment. You can't summarize that with "do you want to be explicit about assumptions".

I find it interesting that the prior probability example always happens to be the one where the prior is correct, while the opposite direction is never given more than half a line. You can very easily turn it around so that an unreasonably large amount of evidence is needed to move a strongly believed wrong prior.

My favourite example is to do that with normal distributions and the posterior ends up as "let's meet in the middle and agree that we both were wrong" -a very low variance posterior with mean right in the middle between prior and data.