Abstract
AbstractHigh-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 the 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.
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science
Reference34 articles.
1. Bédard, M.: Weak convergence of Metropolis algorithms for non-i.i.d. target distributions. Ann. Appl. Probab. 17, 1222–1244 (2007)
2. Bédard, M., Douc, R., Moulines, E.: Scaling analysis of multiple-try MCMC methods. Stochastic Process. Appl. 122(3), 758–786 (2012)
3. Belloni, A., Chernozhukov, V.: On the computational complexity of MCMC-based estimators in large samples. Ann. Stat. 37(4), 2011–2055 (2009)
4. Belloni, A., Chernozhukov, V.: Posterior inference in curved exponential families under increasing dimensions. Econ. J. 17(2), S75–S100 (2014)
5. Beskos, A., Pillai, N., Roberts, G.O., Sanz-Serna, J.-M., Stuart, A.M.: Optimal tuning of the hybrid Monte Carlo algorithm. Bernoulli 19(5A), 1501–1534 (2013)
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