Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks
Abstract
We formulate in this paper the mini-bucket algorithm for approximate inference in terms of exact inference on an approximate model produced by splitting nodes in a Bayesian network. The new formulation leads to a number of theoretical and practical implications. First, we show that branch-and-bound search algorithms that use mini-bucket bounds may operate in a drastically reduced search space. Second, we show that the proposed formulation inspires new mini-bucket heuristics and allows us to analyze existing heuristics from a new perspective. Finally, we show that this new formulation allows mini-bucket approximations to benefit from recent advances in exact inference, allowing one to significantly increase the reach of these approximations.
Cite
Text
Choi et al. "Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 2007. doi:10.5555/3020488.3020496Markdown
[Choi et al. "Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks." Conference on Uncertainty in Artificial Intelligence, 2007.](https://mlanthology.org/uai/2007/choi2007uai-node/) doi:10.5555/3020488.3020496BibTeX
@inproceedings{choi2007uai-node,
title = {{Node Splitting: A Scheme for Generating Upper Bounds in Bayesian Networks}},
author = {Choi, Arthur and Chavira, Mark and Darwiche, Adnan},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2007},
pages = {57-66},
doi = {10.5555/3020488.3020496},
url = {https://mlanthology.org/uai/2007/choi2007uai-node/}
}