Abstract
This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and effciency. 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.