Exploiting Probabilistic Independence for Permutations

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Jonathan Huang, Carlos Guestrin, Xiaoye Jiang, Leonidas Guibas ;
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:248-255, 2009.

Abstract

Permutations are ubiquitous in many real world problems, such as voting, rankings and data association. Representing uncertainty over permutations is challenging, since there are n! possibilities. Recent Fourier-based approaches can be used to provide a compact representation over low-frequency components of the distribution. Though polynomial, the complexity of these representations grows very rapidly, especially if we want to maintain reasonable estimates for peaked distributions. In this paper, we first characterize the notion of probabilistic independence for distribution over permutations. We then present a method for factoring distributions into independent components in the Fourier domain, and use our algorithms to decompose large problems into much smaller ones. We demonstrate that our method provides very significant improvements in terms of running time, on real tracking data.

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