Exploiting Functional Dependence in Bayesian Network Inference
  We propose an efficient method for Bayesian network inference in models with
functional dependence. We generalize the multiplicative factorization method
originally designed by Takikawa and D Ambrosio(1999) FOR models WITH
independence OF causal influence.Using a hidden variable, we transform a
probability potential INTO a product OF two - dimensional potentials.The
multiplicative factorization yields more efficient inference. FOR example, IN
junction tree propagation it helps TO avoid large cliques. IN ORDER TO keep
potentials small, the number OF states OF the hidden variable should be
minimized.We transform this problem INTO a combinatorial problem OF minimal
base IN a particular space.We present an example OF a computerized adaptive
test, IN which the factorization method IS significantly more efficient than
previous inference methods.
