  A Chain Event Graph (CEG) is a graphial model which designed to embody
conditional independencies in problems whose state spaces are highly asymmetric
and do not admit a natural product structure. In this paer we present a
probability propagation algorithm which uses the topology of the CEG to build a
transporter CEG. Intriungly,the transporter CEG is directly analogous to the
triangulated Bayesian Network (BN) in the more conventional junction tree
propagation algorithms used with BNs. The propagation method uses factorization
formulae also analogous to (but different from) the ones using potentials on
cliques and separators of the BN. It appears that the methods will be typically
more efficient than the BN algorithms when applied to contexts where there is
significant asymmetry present.
