Fiducial probability presents another way in which classical statistics can be given an epistemic status. Fisher (1930, 1933, 1935c, 1956/1973) developed the notion of fiducial probability as a way of deriving a probability assignment over hypotheses without assuming a prior probability over statistical hypotheses at the outset. The fiducial argument is controversial, and it is generally agreed that its applicability is limited to particular statistical problems. Dempster (1964), Hacking (1965), Edwards (1972), Seidenfeld (1996) and Zabell (1996) provide insightful discussions. Seidenfeld (1979) presents a particularly detailed study and a further discussion of the restricted applicability of the argument in cases with multiple parameters. Dawid and Stone (1982) argue that in order to run the fiducial argument, one has to assume that the statistical problem can be captured in a functional model that is smoothly invertible. Dempster (1966) provides generalizations of this idea for cases in which the distribution over \(\theta\) is not fixed uniquely but only constrained within upper and lower bounds (cf. Haenni et al 2011). Crucially, such constraints on the probability distribution over values of \(\theta\) are obtained without assuming any distribution over \(\theta\) at the outset.