  Probability-like parameters appearing in some statistical models, and their
prior distributions, are reinterpreted through the notion of `circumstance', a
term which stands for any piece of knowledge that is useful in assigning a
probability and that satisfies some additional logical properties. The idea,
which can be traced to Laplace and Jaynes, is that the usual inferential
reasonings about the probability-like parameters of a statistical model can be
conceived as reasonings about equivalence classes of `circumstances' - viz.,
real or hypothetical pieces of knowledge, like e.g. physical hypotheses, that
are useful in assigning a probability and satisfy some additional logical
properties - that are uniquely indexed by the probability distributions they
lead to.
