Markov bases: a 25 year update
Methodology
2024-01-10 v3 Commutative Algebra
Combinatorics
Abstract
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.
Cite
@article{arxiv.2306.06270,
title = {Markov bases: a 25 year update},
author = {Félix Almendra-Hernández and Jesús A. De Loera and Sonja Petrović},
journal= {arXiv preprint arXiv:2306.06270},
year = {2024}
}
Comments
24 pages, 3 figures