On Finding Relevant Variables in Discrete Bayesian Network Inference

A central task in discrete Bayesian network (BN) inference is to determine those variables relevant to answer a given query. Two linear algorithms for this task explore the possibly relevant and active parts of a BN, respectively. We empirically compare these two methods along with a variation of each.