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Main Author: Moumeni, Nordine
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2404.11432
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author Moumeni, Nordine
author_facet Moumeni, Nordine
contents We study time-inhomogeneous Markov chains to obtain quantitative results on their asymptotic behavior. We use Poincaré, Nash, and logarithmic-Sobolev inequalities. We assume that our Markov chain admits a finite invariant measure at each time and that the sequence of these invariant measures is non-decreasing. We deduce quantitative bounds on the merging time of the distributions for the chain started at two arbitrary points and we illustrate these new results with examples.
format Preprint
id arxiv_https___arxiv_org_abs_2404_11432
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Quantitative merging for time-inhomogeneous Markov chains in non-decreasing environments via functional inequalities
Moumeni, Nordine
Probability
We study time-inhomogeneous Markov chains to obtain quantitative results on their asymptotic behavior. We use Poincaré, Nash, and logarithmic-Sobolev inequalities. We assume that our Markov chain admits a finite invariant measure at each time and that the sequence of these invariant measures is non-decreasing. We deduce quantitative bounds on the merging time of the distributions for the chain started at two arbitrary points and we illustrate these new results with examples.
title Quantitative merging for time-inhomogeneous Markov chains in non-decreasing environments via functional inequalities
topic Probability
url https://arxiv.org/abs/2404.11432