| Issue |
Europhys. Lett.
Volume 76, Number 3, November 2006
|
|
|---|---|---|
| Page(s) | 353 - 359 | |
| Section | General | |
| DOI | https://doi.org/10.1209/epl/i2006-10287-1 | |
| Published online | 29 September 2006 | |
An adaptive Metropolis-Hastings scheme: Sampling and optimization
1
NASA Ames Research Center - Mail Stop 269-1, Moffett Field, CA 94035-1000, USA
2
Physics Department, Clarendon Laboratory, Oxford University - Oxford OX1 3PU, UK
Received:
31
May
2006
Accepted:
11
September
2006
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 approximation. 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.
PACS: 02.70.Tt – Justifications or modifications of Monte Carlo methods / 05.10.Ln – Monte Carlo methods / 02.70.Uu – Applications of Monte Carlo methods
© EDP Sciences, 2006
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