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 algorithms 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 inference algorithms, particularly Bucket Elimination, a special case of cluster (join) tree decomposition, to utilize disk memory. We provide the underlying ideas and show promising 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 algorithms 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 inference algorithms, particularly Bucket Elimination, a special case of cluster (join) tree decomposition, to utilize disk memory. We provide the underlying ideas and show promising 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 algorithms 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 inference algorithms, particularly Bucket Elimination, a special case of cluster (join) tree decomposition, to utilize disk memory. We provide the underlying ideas and show promising 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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