Random walk Metropolis algorithm in high dimension with non-Gaussian target distributions

Kengo Kamatani

Stochastic Processes and their Applications, 2020, vol. 130, issue 1, 297-327

Abstract: High-dimensional asymptotics of the random walk Metropolis–Hastings algorithm are well understood for a class of light-tailed target distributions. Although this idealistic assumption is instructive, it may not always be appropriate, especially for complicated target distributions. We here study heavy-tailed target distributions for the random walk Metropolis algorithms. When the number of dimensions is d, the rate of consistency is d2 and the calculation cost is O(d3), which might be too expensive in high dimension.

Keywords: Markov chain; Diffusion limit; Consistency; Monte Carlo; Stein’s method (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:spapps:v:130:y:2020:i:1:p:297-327

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DOI: 10.1016/j.spa.2019.03.002

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