An adaptive Metropolis-Hastings scheme: sampling and optimization

Details An adaptive Metropolis-Hastings scheme: sampling and optimization An adaptive Metropolis-Hastings scheme: sampling and optimization

2005-04-07

arxiv.org/abs/cond-mat/0504163v1

Europhysics Letters 76, 353-359 (2006)

Physics

Condensed Matter

Other Condensed Matter

To appear in Europhysics Letters

Scientific paper

10.1209/epl/i2006-10287-1

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.

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