  We present an algorithm for arc reversal in Bayesian networks with
tree-structured conditional probability tables, and consider some of its
advantages, especially for the simulation of dynamic probabilistic networks. In
particular, the method allows one to produce CPTs for nodes involved in the
reversal that exploit regularities in the conditional distributions. We argue
that this approach alleviates some of the overhead associated with arc
reversal, plays an important role in evidence integration and can be used to
restrict sampling of variables in DPNs. We also provide an algorithm that
detects the dynamic irrelevance of state variables in forward simulation. This
algorithm exploits the structured CPTs in a reversed network to determine, in a
time-independent fashion, the conditions under which a variable does or does
not need to be sampled.
