  This paper describes a class of probabilistic approximation algorithms based
on bucket elimination which offer adjustable levels of accuracy and efficiency.
We analyze the approximation for several tasks: finding the most probable
explanation, belief updating and finding the maximum a posteriori hypothesis.
We identify regions of completeness and provide preliminary empirical
evaluation on randomly generated networks.
