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October 2005 Distance-reducing Markov bases for sampling from a discrete sample space
Akimichi Takemura, Satoshi Aoki
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Bernoulli 11(5): 793-813 (October 2005). DOI: 10.3150/bj/1130077594

Abstract

We study Markov bases for sampling from a discrete sample space equipped with a convenient metric. Starting from any two states in the sample space, we ask whether we can always move closer by an element of a Markov basis. We call a Markov basis distance-reducing if this is the case. The particular metric we consider in this paper is the L1-norm on the sample space. Some characterizations of L1-norm-reducing Markov bases are derived.

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Akimichi Takemura. Satoshi Aoki. "Distance-reducing Markov bases for sampling from a discrete sample space." Bernoulli 11 (5) 793 - 813, October 2005. https://doi.org/10.3150/bj/1130077594

Information

Published: October 2005
First available in Project Euclid: 23 October 2005

zbMATH: 1085.62076
MathSciNet: MR2172841
Digital Object Identifier: 10.3150/bj/1130077594

Keywords: Contingency tables , Graver bases , Gröbner bases , L_1-norm , Markov chain Monte Carlo , toric ideals

Rights: Copyright © 2005 Bernoulli Society for Mathematical Statistics and Probability

Vol.11 • No. 5 • October 2005
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