  A difficult task in modeling with Bayesian networks is the elicitation of
numerical parameters of Bayesian networks. A large number of parameters is
needed to specify a conditional probability table (CPT) that has a larger
parent set. In this paper we show that, most CPTs from real applications of
Bayesian networks can actually be very well approximated by tables that require
substantially less parameters. This observation has practical consequence not
only for model elicitation but also for efficient probabilistic reasoning with
these networks.

Artificial Intelligence