Probability matrices, non-negative rank, and parameterizations of mixture models
Computation
2009-11-10 v2
Abstract
In this paper we parameterize non-negative matrices of sum one and rank at most two. More precisely, we give a family of parameterizations using the least possible number of parameters. We also show how these parameterizations relate to a class of statistical models, known in Probability and Statistics as mixture models for contingency tables.
Cite
@article{arxiv.0911.0412,
title = {Probability matrices, non-negative rank, and parameterizations of mixture models},
author = {Enrico Carlini and Fabio Rapallo},
journal= {arXiv preprint arXiv:0911.0412},
year = {2009}
}