An adaptive Metropolis-Hastings scheme: sampling and optimization

Thumbnail Image

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

We 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.
To appear in Europhysics Letters

Keywords

Other Condensed Matter, Disordered Systems and Neural Networks

Citation

URI

http://salesiana.dossiersoluciones.com/handle/123456789/99209

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cond-mat/0504163
 http://arxiv.org/abs/cond-mat/0504163
 Europhysics Letters 76, 353-359 (2006)
 doi:10.1209/epl/i2006-10287-1

Collections

arXiv