Permutations are ubiquitous in many real-world problems, such as voting, ranking, and data association.Representing uncertainty over permutations is challenging, since there are n! possibilities,
and typical compact and factorized probability distribution representations, such as graphical models,cannot capture the mutual exclusivity constraints associated with permutations.
This toolbox provides some of the key algorithm and examples as decribed in the paper
"Fourier Theoretic Probabilistic Inference over Permutations",
Jonathan Huang, Carlos Guestrin, Leonidas Guibas.
Journal of Machine Learning (JMLR), Volume 10 pp. 997-1070, May 2009.
Keywords: identity management, permutations, approximate inference, group theoretical methods,sensor networks