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Main Author: Daures, Léo
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2604.21663
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author Daures, Léo
author_facet Daures, Léo
contents We establish the weak large deviations principle for empirical measures of Markov chains on $\mathbb R^d$ under mild assumptions. In particular, no irreducibility is assumed and the initial measure may be arbitrary. The proof is entirely self-contained and relies on subadditivity. In the absence of irreducibility, examples show that the rate function is not convex in general.
format Preprint
id arxiv_https___arxiv_org_abs_2604_21663
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Large deviations for non-irreducible Markov chains on Euclidean spaces
Daures, Léo
Probability
We establish the weak large deviations principle for empirical measures of Markov chains on $\mathbb R^d$ under mild assumptions. In particular, no irreducibility is assumed and the initial measure may be arbitrary. The proof is entirely self-contained and relies on subadditivity. In the absence of irreducibility, examples show that the rate function is not convex in general.
title Large deviations for non-irreducible Markov chains on Euclidean spaces
topic Probability
url https://arxiv.org/abs/2604.21663