Importance sampling for weighted binary random matrices with specified margins
Computation
2013-01-18 v1 Combinatorics
Abstract
A sequential importance sampling algorithm is developed for the distribution that results when a matrix of independent, but not identically distributed, Bernoulli random variables is conditioned on a given sequence of row and column sums. This conditional distribution arises in a variety of applications and includes as a special case the uniform distribution over zero-one tables with specified margins. The algorithm uses dynamic programming to combine hard margin constraints, combinatorial approximations, and additional non-uniform weighting in a principled way to give state-of-the-art results.
Cite
@article{arxiv.1301.3928,
title = {Importance sampling for weighted binary random matrices with specified margins},
author = {Matthew T. Harrison and Jeffrey W. Miller},
journal= {arXiv preprint arXiv:1301.3928},
year = {2013}
}
Comments
39 pages (13 pages main text, 26 pages supplementary material); supersedes arXiv:0906.1004