Abstract
The class of algorithms for approximating reasoning tasks presented in this paper is based on approximating the general bucket elimination framework. The algorithms have adjustable levels of accuracy and efficiency, and they can be applied uniformly across many areas and problem tasks. We introduce these algorithms in the context of combinatorial optimization and probabilistic inference.