Approximating the Partition Function by Deleting and then Correcting for Model Edges
Machine Learning
2012-06-18 v1 Machine Learning
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
@article{arxiv.1206.3241,
title = {Approximating the Partition Function by Deleting and then Correcting for Model Edges},
author = {Arthur Choi and Adnan Darwiche},
journal= {arXiv preprint arXiv:1206.3241},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)