Random sampling of contingency tables via probabilistic divide-and-conquer

We present a new approach for random sampling of contingency tables of any size and constraints based on a recently introduced probabilistic divide-and-conquer (PDC) technique. Our first application is a recursive PDC: it samples the least significant bit of each entry in the table, motivated by the...

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Bibliographic Details
Published in:	Computational statistics Vol. 35; no. 2; pp. 837 - 869
Main Authors:	DeSalvo, Stephen, Zhao, James
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2020 Springer Nature B.V
Subjects:	Algorithms Contingency Contingency tables Economic Theory/Quantitative Economics/Mathematical Methods Mathematics and Statistics Monte Carlo simulation Original Paper Probability and Statistics in Computer Science Probability theory Probability Theory and Stochastic Processes Random sampling Random variables Sampling techniques Statistics Exact sampling Boltzmann sampler Transportation polytope Approximate sampling
ISSN:	0943-4062, 1613-9658
Online Access:	Get full text
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