BEEM : Bucket Elimination with External Memory

Kalev Kask, Rina Dechter, Andrew Gelfand
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:276-284, 2010.

Abstract

A major limitation of exact inference algo- rithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend in- ference algorithms, particularly Bucket Elim- ination, a special case of cluster (join) tree de- composition, to utilize disk memory. We pro- vide the underlying ideas and show promis- ing empirical results of exactly solving large problems not solvable before.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-kask10a, title = {{BEEM} : Bucket Elimination with External Memory}, author = {Kask, Kalev and Dechter, Rina and Gelfand, Andrew}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {276--284}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/kask10a/kask10a.pdf}, url = {https://proceedings.mlr.press/r8/kask10a.html}, abstract = {A major limitation of exact inference algo- rithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend in- ference algorithms, particularly Bucket Elim- ination, a special case of cluster (join) tree de- composition, to utilize disk memory. We pro- vide the underlying ideas and show promis- ing empirical results of exactly solving large problems not solvable before.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T BEEM : Bucket Elimination with External Memory %A Kalev Kask %A Rina Dechter %A Andrew Gelfand %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-kask10a %I PMLR %P 276--284 %U https://proceedings.mlr.press/r8/kask10a.html %V R8 %X A major limitation of exact inference algo- rithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend in- ference algorithms, particularly Bucket Elim- ination, a special case of cluster (join) tree de- composition, to utilize disk memory. We pro- vide the underlying ideas and show promis- ing empirical results of exactly solving large problems not solvable before. %Z Reissued by PMLR on 04 October 2026.
APA
Kask, K., Dechter, R. & Gelfand, A.. (2010). BEEM : Bucket Elimination with External Memory. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:276-284 Available from https://proceedings.mlr.press/r8/kask10a.html. Reissued by PMLR on 04 October 2026.

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