Uncertainty in Artificial Intelligence
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Approximating the Partition Function by Deleting and then Correcting for Model Edges
Arthur Choi, Adnan Darwiche
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.
Keywords:
Pages: 79-87
PS Link:
PDF Link: /papers/08/p79-choi.pdf
BibTex:
@INPROCEEDINGS{Choi08,
AUTHOR = "Arthur Choi and Adnan Darwiche",
TITLE = "Approximating the Partition Function by Deleting and then Correcting for Model Edges",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "79--87"
}


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