Independence of Causal Influence and Clique Tree Propagation
  This paper explores the role of independence of causal influence (ICI) in
Bayesian network inference. ICI allows one to factorize a conditional
probability table into smaller pieces. We describe a method for exploiting the
factorization in clique tree propagation (CTP) - the state-of-the-art exact
inference algorithm for Bayesian networks. We also present empirical results
showing that the resulting algorithm is significantly more efficient than the
combination of CTP and previous techniques for exploiting ICI.
