Random Sampling of Contingency Tables via Probabilistic Divide-and-Conquer

Abstract

We present a new approach for random sampling of contingency tables of any size and constraints based on a recently introduced $\text{probabilistic divide-and-conquer}$ technique. A simple exact sampling algorithm is presented for $2\times n$ tables, as well as a generalization where each entry of the table has a specified marginal distribution.

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