There is also a response that avoids the choice of a prior altogether. This response starts with the same idea as hierarchical models: rather than considering a single prior over the hypotheses in the model, we consider a parameterized set of them. But instead of defining a distribution over this set, proponents of interval-valued or imprecise probability claim that our epistemic state regarding the priors is better expressed by this set of distributions, and that sharp probability assignments must therefore be replaced by lower and upper bounds to the assignments. Now the idea that uncertain opinion is best captured by a set of probability assignments, or a credal set for short, has a long history and is backed by an extensive literature (e.g., De Finetti 1974, Levi 1980, Dempster 1967 and 1968, Shafer 1976, Walley 1991). In light of the main debate in the philosophy of statistics, the use of interval-valued priors indeed forms an attractive extension of Bayesian statistics: it allows us to refrain from choosing a specific prior, and thereby presents a rapprochement to the classical view on statistics.