Markov bases: a 25 year update

Fuente: arXiv
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Main Authors: Almendra-Hernández, Félix, De Loera, Jesús A., Petrović, Sonja
Format: Preprint
Published: 2023
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author Almendra-Hernández, Félix
De Loera, Jesús A.
Petrović, Sonja
author_facet Almendra-Hernández, Félix
De Loera, Jesús A.
Petrović, Sonja
contents In this paper, we evaluate the challenges and best practices associated with the Markov bases approach to sampling from conditional distributions. We provide insights and clarifications after 25 years of the publication of the fundamental theorem for Markov bases by Diaconis and Sturmfels. In addition to a literature review we prove three new results on the complexity of Markov bases in hierarchical models, relaxations of the fibers in log-linear models, and limitations of partial sets of moves in providing an irreducible Markov chain.
format Preprint
id arxiv_https___arxiv_org_abs_2306_06270
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Markov bases: a 25 year update
Almendra-Hernández, Félix
De Loera, Jesús A.
Petrović, Sonja
Methodology
Commutative Algebra
Combinatorics
62R01, 62-08, 62P10, 62H17
In this paper, we evaluate the challenges and best practices associated with the Markov bases approach to sampling from conditional distributions. We provide insights and clarifications after 25 years of the publication of the fundamental theorem for Markov bases by Diaconis and Sturmfels. In addition to a literature review we prove three new results on the complexity of Markov bases in hierarchical models, relaxations of the fibers in log-linear models, and limitations of partial sets of moves in providing an irreducible Markov chain.
title Markov bases: a 25 year update
topic Methodology
Commutative Algebra
Combinatorics
62R01, 62-08, 62P10, 62H17
url https://arxiv.org/abs/2306.06270