From what I understand, Bootstrapping is incredibly useful in a Frequentist setting. In frequentist stats: we are trying to estimate long-run probabilities. In practice, we do not have an infinite number of samples. The bootstrap allows us to simulate an infinite number of re-samples. From what I understand, this is probably the most useful tool in Frequentist statistics.
Is the bootstrapping procedure essentially useless to a Bayesian? Bayesians only rely beliefs, and by resampling the original data: I doubt the belief would change.
Is the bootstrap useless in the Bayesian school of stats?
Although there exists a "Bayesian bootstrap", I am referring specifically to the Frequentist bootstrap.