Weak convergence of Metropolis algorithms for non-i.i.d. target distributions
| dc.creator | Bédard, Mylène | |
| dc.date | 2007-10-19 | |
| dc.date.accessioned | 2026-07-07T08:38:18Z | |
| dc.date.available | 2026-07-07T08:38:18Z | |
| dc.description | 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. | |
| dc.description | 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) | |
| dc.identifier | https://arxiv.org/abs/0710.3684 | |
| dc.identifier | http://arxiv.org/abs/0710.3684 | |
| dc.identifier | Annals of Applied Probability 2007, Vol. 17, No. 4, 1222-1244 | |
| dc.identifier | doi:10.1214/105051607000000096 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/140679 | |
| dc.subject | Probability | |
| dc.subject | 60F05 (Primary) 65C40 (Secondary) | |
| dc.title | Weak convergence of Metropolis algorithms for non-i.i.d. target distributions | |
| dc.type | text |