A Standard Approach for Optimizing Belief Network Inference Using Query DAGs
Abstract
This paper proposes a novel, algorithm-independent approach to optimizing belief network inference. Rather than designing optimizations on an algorithm by algorithm basis, we argue that one should use an unoptimized algorithm to generate a Q-DAG, a compiled graphical representation of the belief network, and then optimize the Q-DAG and its evaluator instead. We present a set of Q-DAG optimizations that supplant optimizations designed for traditional inference algorithms, including zero compression, network pruning and caching. We show that our Q-DAG optimizations require time linear in the Q-DAG size, and significantly simplify the process of designing algorithms for optimizing belief network inference.
Cite
Text
Darwiche and Provan. "A Standard Approach for Optimizing Belief Network Inference Using Query DAGs." Conference on Uncertainty in Artificial Intelligence, 1997.Markdown
[Darwiche and Provan. "A Standard Approach for Optimizing Belief Network Inference Using Query DAGs." Conference on Uncertainty in Artificial Intelligence, 1997.](https://mlanthology.org/uai/1997/darwiche1997uai-standard/)BibTeX
@inproceedings{darwiche1997uai-standard,
title = {{A Standard Approach for Optimizing Belief Network Inference Using Query DAGs}},
author = {Darwiche, Adnan and Provan, Gregory M.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1997},
pages = {116-123},
url = {https://mlanthology.org/uai/1997/darwiche1997uai-standard/}
}