A Scheme for Approximating Probabilistic Inference
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
Text
Dechter and Rish. "A Scheme for Approximating Probabilistic Inference." Conference on Uncertainty in Artificial Intelligence, 1997.Markdown
[Dechter and Rish. "A Scheme for Approximating Probabilistic Inference." Conference on Uncertainty in Artificial Intelligence, 1997.](https://mlanthology.org/uai/1997/dechter1997uai-scheme/)BibTeX
@inproceedings{dechter1997uai-scheme,
title = {{A Scheme for Approximating Probabilistic Inference}},
author = {Dechter, Rina and Rish, Irina},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1997},
pages = {132-141},
url = {https://mlanthology.org/uai/1997/dechter1997uai-scheme/}
}