Given the task definition and input, reply with output. In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and efficiency. We analyze the ap proximation for several tasks: finding the most probable explanation, belief updat ing and finding the maximum a posteriori hypothesis. We identify regions of com pleteness and provide preliminary empiri cal evaluation on randomly generated net
A scheme for approximating probabilistic inference