English

Mixture decompositions of exponential families using a decomposition of their sample spaces

Statistics Theory 2013-02-27 v4 Machine Learning Statistics Theory

Abstract

We study the problem of finding the smallest mm such that every element of an exponential family can be written as a mixture of mm elements of another exponential family. We propose an approach based on coverings and packings of the face lattice of the corresponding convex support polytopes and results from coding theory. We show that m=qN1m=q^{N-1} is the smallest number for which any distribution of NN qq-ary variables can be written as mixture of mm independent qq-ary variables. Furthermore, we show that any distribution of NN binary variables is a mixture of m=2N(k+1)(1+1/(2k1))m = 2^{N-(k+1)}(1+ 1/(2^k-1)) elements of the kk-interaction exponential family.

Keywords

Cite

@article{arxiv.1008.0204,
  title  = {Mixture decompositions of exponential families using a decomposition of their sample spaces},
  author = {Guido Montufar},
  journal= {arXiv preprint arXiv:1008.0204},
  year   = {2013}
}

Comments

17 pages, 2 figures