Approximating the partition function by deleting and then correcting for model edges

Arthur Choi, Adnan Darwiche
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, PMLR R6:79-87, 2008.

Abstract

We propose an approach for approximating the partition function which is based on two steps: (1) computing the partition function of a simplified model which is obtained by deleting model edges, and (2) rectifying the result by applying an edge-by-edge correction. The approach leads to an intuitive framework in which one can trade-off the quality of an approximation with the complexity of computing it. It also includes the Bethe free energy approximation as a degenerate case. We develop the approach theoretically in this paper and provide a number of empirical results that reveal its practical utility.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR6-choi08a, title = {Approximating the partition function by deleting and then correcting for model edges}, author = {Choi, Arthur and Darwiche, Adnan}, booktitle = {Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence}, pages = {79--87}, year = {2008}, editor = {McAllester, David A. and Myllymäki, Petri}, volume = {R6}, series = {Proceedings of Machine Learning Research}, month = {09--12 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r6/main/assets/choi08a/choi08a.pdf}, url = {https://proceedings.mlr.press/r6/choi08a.html}, abstract = {We propose an approach for approximating the partition function which is based on two steps: (1) computing the partition function of a simplified model which is obtained by deleting model edges, and (2) rectifying the result by applying an edge-by-edge correction. The approach leads to an intuitive framework in which one can trade-off the quality of an approximation with the complexity of computing it. It also includes the Bethe free energy approximation as a degenerate case. We develop the approach theoretically in this paper and provide a number of empirical results that reveal its practical utility.}, note = {Reissued by PMLR on 09 October 2024.} }
Endnote
%0 Conference Paper %T Approximating the partition function by deleting and then correcting for model edges %A Arthur Choi %A Adnan Darwiche %B Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2008 %E David A. McAllester %E Petri Myllymäki %F pmlr-vR6-choi08a %I PMLR %P 79--87 %U https://proceedings.mlr.press/r6/choi08a.html %V R6 %X We propose an approach for approximating the partition function which is based on two steps: (1) computing the partition function of a simplified model which is obtained by deleting model edges, and (2) rectifying the result by applying an edge-by-edge correction. The approach leads to an intuitive framework in which one can trade-off the quality of an approximation with the complexity of computing it. It also includes the Bethe free energy approximation as a degenerate case. We develop the approach theoretically in this paper and provide a number of empirical results that reveal its practical utility. %Z Reissued by PMLR on 09 October 2024.
APA
Choi, A. & Darwiche, A.. (2008). Approximating the partition function by deleting and then correcting for model edges. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R6:79-87 Available from https://proceedings.mlr.press/r6/choi08a.html. Reissued by PMLR on 09 October 2024.

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