As If Frequentists somehow didn't need priors. Everyone starts with prior knowledge. We might as well use it. Or do you advocate not using every scrap of knowledge available to you? That would be stupid.
Sure, prior knowledge can be shaky, or difficult to justify. But at least, a Bayesian will be explicit about it, instead of, like, sweeping normal probability distribution assumptions under the linear regression rug.
> it would be a terrible idea to prefer Bayesian approaches to Frequentist ones in all situations.
Name three examples that doesn't involve the Frequentist using better prior information than the Bayesian.
By the way, Bayesians know that using probability theory correctly is sometimes intractable (combinatorial explosion and all that). In those cases, they will use approximations. But at least, they will know it's an approximation.
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You really should read chapters 1 and 2 of Probability Theory: the Logic of Science. They give a good feel of why Bayesians are correct as a simple matter of fact.