  Reacting against the limitation of statistics to decision procedures, R. A.
Fisher proposed for inductive reasoning the use of the fiducial distribution, a
parameter-space distribution of epistemological probability transferred
directly from limiting relative frequencies rather than computed according to
the Bayes update rule. The proposal is developed as follows using the
confidence measure of a scalar parameter of interest. (With the restriction to
one-dimensional parameter space, a confidence measure is essentially a fiducial
probability distribution free of complications involving ancillary statistics.)
  A betting game establishes a sense in which confidence measures are the only
reliable inferential probability distributions. The equality between the
probabilities encoded in a confidence measure and the coverage rates of the
corresponding confidence intervals ensures that the measure's rule for
assigning confidence levels to hypotheses is uniquely minimax in the game.
  Although a confidence measure can be computed without any prior distribution,
previous knowledge can be incorporated into confidence-based reasoning. To
adjust a p-value or confidence interval for prior information, the confidence
measure from the observed data can be combined with one or more independent
confidence measures representing previous agent opinion. (The former confidence
measure may correspond to a posterior distribution with frequentist matching of
coverage probabilities.) The representation of subjective knowledge in terms of
confidence measures rather than prior probability distributions preserves
approximate frequentist validity.
