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A key sticking point of Bayesian analysis is the choice of priordistribution, and there is a vast literature on potential defaults includinguniform priors, Jeffreys' priors, reference priors, maximum entropy priors, andweakly informative priors. These methods, however, often manifest a keyconceptual tension in prior modeling: a model encoding true prior informationshould be chosen without reference to the model of the measurement process, butalmost all common prior modeling techniques are implicitly motivated by areference likelihood. In this paper we resolve this apparent paradox by placingthe choice of prior into the context of the entire Bayesian analysis, frominference to prediction to model evaluation.

The prior can generally only be understood in the context of the likelihood