A Scheme for Approximating Probabilistic Inference
Artificial Intelligence
2013-02-08 v1
Abstract
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.
Cite
@article{arxiv.1302.1534,
title = {A Scheme for Approximating Probabilistic Inference},
author = {Rina Dechter and Irina Rish},
journal= {arXiv preprint arXiv:1302.1534},
year = {2013}
}
Comments
Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)