> But classical, frequentist techniques incorporate priors implicitly: a flat prior indicating we have no information other than the data. That's not completel

> But classical, frequentist techniques incorporate priors implicitly: a flat prior indicating we have no information other than the data. That's not completely right. Frequentists don't assume a flat prior, but rather play a minimax strategy that gives a certain worst-case performance across all possible priors. For example, frequentist confidence intervals have coverage guarantees, while Bayesian intervals generally don't. The middle ground is using "objective Bayesian" methods that aim for good frequentist properties.