Exploiting Within-Clique Factorizations in Junction-Tree Algorithms
Abstract
We show that the expected computational complexity of the Junction-Tree Algorithm for maximum a posteriori inference in graphical models can be improved. Our results apply whenever the potentials over maximal cliques of the triangulated graph are factored over subcliques. This is common in many real applications, as we illustrate with several examples. The new algorithms are easily implemented, and experiments show substantial speed-ups over the classical Junction-Tree Algorithm. This enlarges the class of models for which exact inference is efficient.
Cite
Text
McAuley and Caetano. "Exploiting Within-Clique Factorizations in Junction-Tree Algorithms." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[McAuley and Caetano. "Exploiting Within-Clique Factorizations in Junction-Tree Algorithms." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/mcauley2010aistats-exploiting/)BibTeX
@inproceedings{mcauley2010aistats-exploiting,
title = {{Exploiting Within-Clique Factorizations in Junction-Tree Algorithms}},
author = {McAuley, Julian and Caetano, Tiberio},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {525-532},
volume = {9},
url = {https://mlanthology.org/aistats/2010/mcauley2010aistats-exploiting/}
}