Weak convergence of Metropolis algorithms for non-i.i.d. target distributions
Probability
2007-10-25 v1
Abstract
In this paper, we shall optimize the efficiency of Metropolis algorithms for multidimensional target distributions with scaling terms possibly depending on the dimension. We propose a method for determining the appropriate form for the scaling of the proposal distribution as a function of the dimension, which leads to the proof of an asymptotic diffusion theorem. We show that when there does not exist any component with a scaling term significantly smaller than the others, the asymptotically optimal acceptance rate is the well-known 0.234.
Cite
@article{arxiv.0710.3684,
title = {Weak convergence of Metropolis algorithms for non-i.i.d. target distributions},
author = {Mylène Bédard},
journal= {arXiv preprint arXiv:0710.3684},
year = {2007}
}
Comments
Published in at http://dx.doi.org/10.1214/105051607000000096 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)