  In the canonical examples underlying Shafer-Dempster theory, beliefs over the
hypotheses of interest are derived from a probability model for a set of
auxiliary hypotheses. Beliefs are derived via a compatibility relation
connecting the auxiliary hypotheses to subsets of the primary hypotheses. A
belief function differs from a Bayesian probability model in that one does not
condition on those parts of the evidence for which no probabilities are
specified. The significance of this difference in conditioning assumptions is
illustrated with two examples giving rise to identical belief functions but
different Bayesian probability distributions.
