On the Partition Function and Random Maximum A-Posteriori Perturbations
Abstract
In this paper we relate the partition function to the max-statistics of random variables. In particular, we provide a novel framework for approximating and bounding the partition function using MAP inference on randomly perturbed models. As a result, we can use efficient MAP solvers such as graph-cuts to evaluate the corresponding partition function. We show that our method excels in the typical "high signal - high coupling" regime that results in ragged energy landscapes difficult for alternative approaches.
Cite
Text
Hazan and Jaakkola. "On the Partition Function and Random Maximum A-Posteriori Perturbations." International Conference on Machine Learning, 2012.Markdown
[Hazan and Jaakkola. "On the Partition Function and Random Maximum A-Posteriori Perturbations." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/hazan2012icml-partition/)BibTeX
@inproceedings{hazan2012icml-partition,
title = {{On the Partition Function and Random Maximum A-Posteriori Perturbations}},
author = {Hazan, Tamir and Jaakkola, Tommi S.},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/hazan2012icml-partition/}
}