An adaptive Metropolis-Hastings scheme: sampling and optimization

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dc.creatorWolpert, David H.
dc.creatorLee, Chiu Fan
dc.date2005-04-07
dc.date.accessioned2026-07-07T06:33:29Z
dc.date.available2026-07-07T06:33:29Z
dc.descriptionWe propose an adaptive Metropolis-Hastings algorithm in which sampled data are used to update the proposal distribution. We use the samples found by the algorithm at a particular step to form the information-theoretically optimal mean-field approximation to the target distribution, and update the proposal distribution to be that approximatio. We employ our algorithm to sample the energy distribution for several spin-glasses and we demonstrate the superiority of our algorithm to the conventional MH algorithm in sampling and in annealing optimization.
dc.descriptionTo appear in Europhysics Letters
dc.identifierhttps://arxiv.org/abs/cond-mat/0504163
dc.identifierhttp://arxiv.org/abs/cond-mat/0504163
dc.identifierEurophysics Letters 76, 353-359 (2006)
dc.identifierdoi:10.1209/epl/i2006-10287-1
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/99209
dc.subjectOther Condensed Matter
dc.subjectDisordered Systems and Neural Networks
dc.titleAn adaptive Metropolis-Hastings scheme: sampling and optimization
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