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Weak convergence of metropolis algorithms for non-i.i.d. target distributions
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Bedard, Mylene (2007) Weak convergence of metropolis algorithms for non-i.i.d. target distributions. Annals of Applied Probability, Vol.17 (No.4). pp. 1222-1244. doi:10.1214/105051607000000096 ISSN 1050-5164.
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Official URL: http://dx.doi.org/10.1214/105051607000000096
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.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Annals of Applied Probability | ||||
Publisher: | Institute of Mathematical Statistics | ||||
ISSN: | 1050-5164 | ||||
Official Date: | August 2007 | ||||
Dates: |
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Volume: | Vol.17 | ||||
Number: | No.4 | ||||
Number of Pages: | 23 | ||||
Page Range: | pp. 1222-1244 | ||||
DOI: | 10.1214/105051607000000096 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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