On tensor rank of conditional probability tables in Bayesian networks
This reduces the burden of model elicitation and improves efficiency in probabilistic reasoning for practitioners using Bayesian networks.
The paper addresses the challenge of parameter elicitation in Bayesian networks by showing that conditional probability tables (CPTs) from real applications can be approximated with significantly fewer parameters, reducing the number needed for specification.
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.