Quasi-stationary distributions in reducible state spaces

Abstract We study quasi-stationary distributions and quasi-limiting behaviour of Markov chains in general reducible state spaces with absorption. First, we consider state spaces that can be decomposed into two successive subsets (with communication possible in a single direction), differentiating between three situations, and characterize the exponential order of magnitude and the exact polynomial correction, called the polynomial convergence parameter, for the leading-order term of the semigroup for large time. Second, we consider general Markov chains with finitely or countably many communication classes by applying the first results iteratively over the communication classes of the chain. We conclude with an application of these results to the case of denumerable state spaces, where we prove existence for a quasi-stationary distribution without assuming irreducibility before absorption, but only aperiodicity, existence of a Lyapunov function, and existence of a point with almost surely finite return time.

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Publication Details

Journal
Advances in Applied Probability
Published
2025-12-26
DOI
https://doi.org/10.1017/apr.2025.10045
Citations
1
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
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article

Quasi-stationary distributions in reducible state spaces

Nicolas Champagnat, Denis Villemonais
1 citations
Advances in Applied Probability
Markov Chains and Monte Carlo Methods
article

Quasi-stationary distributions in reducible state spaces

Nicolas Champagnat, Denis Villemonais
article en
1 citations

Abstract

Abstract We study quasi-stationary distributions and quasi-limiting behaviour of Markov chains in general reducible state spaces with absorption. First, we consider state spaces that can be decomposed into two successive subsets (with communication possible in a single direction), differentiating between three situations, and characterize the exponential order of magnitude and the exact polynomial correction, called the polynomial convergence parameter, for the leading-order term of the semigroup for large time. Second, we consider general Markov chains with finitely or countably many communication classes by applying the first results iteratively over the communication classes of the chain. We conclude with an application of these results to the case of denumerable state spaces, where we prove existence for a quasi-stationary distribution without assuming irreducibility before absorption, but only aperiodicity, existence of a Lyapunov function, and existence of a point with almost surely finite return time.

Advances in Applied Probability
Institut Universitaire de France (FR), Université de Lorraine (FR)
Openalex Percentile: Top 100%
Markov Chains and Monte Carlo Methods
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