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Rapidly mixing Markov chains for sampling contingency tables with a constant number of rows

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5 Author(s)

We consider the problem of sampling almost uniformly from the set of contingency tables with given row and column sums, when the number of rows is a constant. (2002) have recently given a fully polynomial randomized approximation scheme (fpras) for the related counting problem, which only employs Markov chain methods indirectly. But they leave open the question as to whether a natural Markov chain on such tables mixes rapidly. Here we answer this question in the affirmative, and hence provide a very different proof of the main result of Cryan and Dyer. We show that the "2 × 2 heat-bath" Markov chain is rapidly mixing. We prove this by considering first a heat-bath chain operating on a larger window. Using techniques developed by Morris and Sinclair (2002) (see also Morris (2002)) for the multidimensional knapsack problem, we show that this chain mixes rapidly. We then apply the comparison method of Diaconis and Saloff-Coste (1993) to show that the 2 × 2 chain is rapidly mixing. As part of our analysis, we give the first proof that the 2 × 2 chain mixes in time polynomial in the input size when both the number of rows and the number of columns is constant.

Published in:

Foundations of Computer Science, 2002. Proceedings. The 43rd Annual IEEE Symposium on

Date of Conference:

2002