Markov Chains of Infinite Order and Asymptotic Satisfaction of Balance: Application to the Adaptive Integration Method

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dc.creatorEarl, David J.
dc.creatorDeem, Michael W.
dc.date2004-11-17
dc.date.accessioned2026-07-07T05:53:27Z
dc.date.available2026-07-07T05:53:27Z
dc.descriptionAdaptive Monte Carlo methods can be viewed as implementations of Markov chains with infinite memory. We derive a general condition for the convergence of a Monte Carlo method whose history dependence is contained within the simulated density distribution. In convergent cases, our result implies that the balance condition need only be satisfied asymptotically. As an example, we show that the adaptive integration method converges.
dc.description13 pages
dc.identifierhttps://arxiv.org/abs/physics/0411150
dc.identifierhttp://arxiv.org/abs/physics/0411150
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/86648
dc.subjectComputational Physics
dc.subjectStatistical Mechanics
dc.titleMarkov Chains of Infinite Order and Asymptotic Satisfaction of Balance: Application to the Adaptive Integration Method
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