Quasi-stationary distributions in reducible state spaces
Nicolas Champagnat (BIGS, IECL), Denis Villemonais (BIGS, IECL, IUF)

TL;DR
This paper analyzes quasi-stationary distributions in reducible Markov chains, providing explicit characterizations of convergence speeds, polynomial correction factors, and conditions for existence without irreducibility.
Contribution
It introduces assumptions for reducible state spaces, characterizes polynomial convergence parameters, and extends results to non-irreducible chains with explicit convergence estimates.
Findings
Explicit characterization of polynomial convergence parameters
Complete set of quasi-stationary distributions identified
Proven existence of quasi-stationary distributions without irreducibility
Abstract
We study quasi-stationary distributions and quasi-limiting behavior of Markov chains in general reducible state spaces with absorption. We propose a set of assumptions dealing with particular situations where the state space can be decomposed into three subsets between which communication is only possible in a single direction. These assumptions allow us to characterize the exponential order of magnitude and the exact polynomial correction, called polynomial convergence parameter, for the leading order term of the semigroup for large time. They also provide explicit convergence speeds to this leading order term. We apply these results to general Markov chains with finitely or denumerably many communication classes using a specific induction over the communication classes of the chain. We are able to explicitely characterize the polynomial convergence parameter, to determine the complete…
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