Lazy Propagation in Junction Trees
  The efficiency of algorithms using secondary structures for probabilistic
inference in Bayesian networks can be improved by exploiting independence
relations induced by evidence and the direction of the links in the original
network. In this paper we present an algorithm that on-line exploits
independence relations induced by evidence and the direction of the links in
the original network to reduce both time and space costs. Instead of
multiplying the conditional probability distributions for the various cliques,
we determine on-line which potentials to multiply when a message is to be
produced. The performance improvement of the algorithm is emphasized through
empirical evaluations involving large real world Bayesian networks, and we
compare the method with the HUGIN and Shafer-Shenoy inference algorithms.
