An Axiomatic Framework for Bayesian and Belief-function Propagation
  In this paper, we describe an abstract framework and axioms under which exact
local computation of marginals is possible. The primitive objects of the
framework are variables and valuations. The primitive operators of the
framework are combination and marginalization. These operate on valuations. We
state three axioms for these operators and we derive the possibility of local
computation from the axioms. Next, we describe a propagation scheme for
computing marginals of a valuation when we have a factorization of the
valuation on a hypertree. Finally we show how the problem of computing
marginals of joint probability distributions and joint belief functions fits
the general framework.
