&gt; <i>But classical, frequentist techniques incorporate priors implicitly: a flat prior indicating we have no information other than the data.</i><p>That&#x27;s not completely right. Frequentists don&#x27;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&#x27;t. The middle ground is using &quot;objective Bayesian&quot; methods that aim for good frequentist properties.