Computable Convergence Rates for Subgeometrically Ergodic Markov Chains

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dc.creatorDouc, Randal
dc.creatorMoulines, Eric
dc.creatorSoulier, Philippe
dc.date2005-11-10
dc.date.accessioned2026-07-07T06:51:06Z
dc.date.available2026-07-07T06:51:06Z
dc.descriptionIn this paper, we give quantitative bounds on the $f$-total variation distance from convergence of an Harris recurrent Markov chain on an arbitrary under drift and minorisation conditions implying ergodicity at a sub-geometric rate. These bounds are then specialized to the stochastically monotone case, covering the case where there is no minimal reachable element. The results are illustrated on two examples from queueing theory and Markov Chain Monte Carlo.
dc.identifierhttps://arxiv.org/abs/math/0511273
dc.identifierhttp://arxiv.org/abs/math/0511273
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/104875
dc.subjectProbability
dc.subject60J10
dc.titleComputable Convergence Rates for Subgeometrically Ergodic Markov Chains
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