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arXiv · 1804.05975

Batch size selection for variance estimators in MCMC

Ying Liu·Dootika Vats·James M. Flegal
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Abstract

We consider batch size selection for a general class of multivariate batch means variance estimators, which are computationally viable for high-dimensional Markov chain Monte Carlo simulations. We derive the asymptotic mean squared error for this class of estimators. Further, we propose a parametric technique for estimating optimal batch sizes and discuss practical issues regarding the estimating process. Vector auto-regressive, Bayesian logistic regression, and Bayesian dynamic space-time examples illustrate the quality of the estimation procedure where the proposed optimal batch sizes outperform current batch size selection methods.

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BibTeXRIS

Ying Liu, Dootika Vats, James M. Flegal. 2018-04-16. Batch size selection for variance estimators in MCMC. https://arxiv.org/abs/1804.05975

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