My attempt to summarise the difference in language familiar to computer scientists, is that you can look at the frequentist vs Bayesian debate as being about w

My attempt to summarise the difference in language familiar to computer scientists, is that you can look at the frequentist vs Bayesian debate as being about when a worst-case analysis is preferable to average-case analysis for unknown parameters of a statistical model. There's something you don't know (the parameters). Are you looking to make statements which bound how bad things could be under the worst-case setting of those parameters? Or do you have some idea upfront about how likely different parameter settings are, and want to make statements about them in the "average" case? Rather like with worst-case vs average-case analysis of algorithms, which is more appropriate depends what you're trying to do, and sometimes both are interesting.