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Main Authors: Puhalskii, Anatolii, Shcherbakov, Vadim
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2501.09323
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author Puhalskii, Anatolii
Shcherbakov, Vadim
author_facet Puhalskii, Anatolii
Shcherbakov, Vadim
contents In this paper we establish a diffusion limit for a multivariate continuous time Markov chain whose components are indexed by vertices of a finite graph. The components take values in a common finite set of non-negative integers and evolve subject to a graph based log-linear interaction. We show that if the set of common values of the components expands to the set of all non-negative integers, then a time-scaled and normalised version of the Markov chain converges to a system of interacting Ornstein-Uhlenbeck processes reflected at the origin. This limit is akin to heavy traffic limits in queueing (and our model can be naturally interpreted as a queueing model). Our proof draws on developments in queueing theory and relies on martingale methods.
format Preprint
id arxiv_https___arxiv_org_abs_2501_09323
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A diffusion limit for Markov chains with log-linear interaction on a graph
Puhalskii, Anatolii
Shcherbakov, Vadim
Probability
In this paper we establish a diffusion limit for a multivariate continuous time Markov chain whose components are indexed by vertices of a finite graph. The components take values in a common finite set of non-negative integers and evolve subject to a graph based log-linear interaction. We show that if the set of common values of the components expands to the set of all non-negative integers, then a time-scaled and normalised version of the Markov chain converges to a system of interacting Ornstein-Uhlenbeck processes reflected at the origin. This limit is akin to heavy traffic limits in queueing (and our model can be naturally interpreted as a queueing model). Our proof draws on developments in queueing theory and relies on martingale methods.
title A diffusion limit for Markov chains with log-linear interaction on a graph
topic Probability
url https://arxiv.org/abs/2501.09323