Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics

High-dimensional limit theorems have been shown useful to derive tuning rules for finding the optimal scaling in random-walk Metropolis algorithms. The assumptions under which weak convergence results are proved are however restrictive: the target density is typically assumed to be of a product form...

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Bibliographic Details
Published inarXiv.org
Main Authors Schmon, Sebastian M, Gagnon, Philippe
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.02.2022
Subjects
Algorithms
Asymptotic properties
Convergence
Density
Guidelines
Random walk
Scaling
Tuning
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Summary:High-dimensional limit theorems have been shown useful to derive tuning rules for finding the optimal scaling in random-walk Metropolis algorithms. The assumptions under which weak convergence results are proved are however restrictive: the target density is typically assumed to be of a product form. Users may thus doubt the validity of such tuning rules in practical applications. In this paper, we shed some light on optimal-scaling problems from a different perspective, namely a large-sample one. This allows to prove weak convergence results under realistic assumptions and to propose novel parameter-dimension-dependent tuning guidelines. The proposed guidelines are consistent with previous ones when the target density is close to having a product form, and the results highlight that the correlation structure has to be accounted for to avoid performance deterioration if that is not the case, while justifying the use of a natural (asymptotically exact) approximation to the correlation matrix that can be employed for the very first algorithm run.
ISSN:2331-8422