Via Frequentist inference only seems easy

Bayesian methods are not necessarily more painful that frequentist procedures. The Bayesian estimation procedure requires more from the user (the priors) and has an expensive and complicated convolution step to use the data to relate the priors to the posteriors (unless you are lucky enough to have something like the theory of conjugate distributions to hide this step). The frequentist estimation procedure seems to be as simple as “copy over your empirical observation as your estimate.” That is unless you have significant hidden state, constraints or discreteness (not the same as having priors). When you actually have to justify the frequentist inference steps (versus just benefiting from them) you find you have to at least imaging submitting every possible inference you could make as a set of variables and picking a minimax solution optimizing expected square-error over the unknown quantities while staying in the linear flat of unbiased solutions (itself a complicated check).

Bayesians need to do a better job of wrapping standard simple analyses (you shouldn’t have to learn and fire up Stan for this sort of thing), and we all need to be aware that_proper_ frequentist inference is not always just the common simple procedure of copying over the empirical observations.