Partition-based Anytime Approximation for Belief Updating

The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, called Mini-Clustering (MC) extends the partition-based approximation offered by mini-bucket elimination, to tree de-compositions. The beneet of this extension is that all single variable beliefs are computed (approximately) at once, using a two-phase message-passing process along the cluster tree. The resulting approximation scheme is governed by a controlling parameter, "z" that allows adjustable levels of accuracy and eeciency, in "anytime" style. Empirical evaluation against competing algorithms such as iterative belief propagation and stochas-tic simulation, demonstrate the superiority of the MC approximation scheme for several classes of problems.