Weak convergence of adaptive Markov chain Monte Carlo

Fuente: arXiv
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Autori principali: Brown, Austin, Rosenthal, Jeffrey S.
Natura: Preprint
Pubblicazione: 2024
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author Brown, Austin
Rosenthal, Jeffrey S.
author_facet Brown, Austin
Rosenthal, Jeffrey S.
contents This article develops general conditions for weak convergence of adaptive Markov chain Monte Carlo processes and is shown to imply a weak law of large numbers for bounded Lipschitz continuous functions. This allows an estimation theory for adaptive Markov chain Monte Carlo where previously developed theory in total variation may fail or be difficult to establish. Extensions of weak convergence to general Wasserstein distances are established along with a weak law of large numbers for possibly unbounded Lipschitz functions. Applications are applied to auto-regressive processes in various settings, unadjusted Langevin processes, and adaptive Metropolis-Hastings.
format Preprint
id arxiv_https___arxiv_org_abs_2406_00820
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Weak convergence of adaptive Markov chain Monte Carlo
Brown, Austin
Rosenthal, Jeffrey S.
Statistics Theory
60J05, 60J22,
This article develops general conditions for weak convergence of adaptive Markov chain Monte Carlo processes and is shown to imply a weak law of large numbers for bounded Lipschitz continuous functions. This allows an estimation theory for adaptive Markov chain Monte Carlo where previously developed theory in total variation may fail or be difficult to establish. Extensions of weak convergence to general Wasserstein distances are established along with a weak law of large numbers for possibly unbounded Lipschitz functions. Applications are applied to auto-regressive processes in various settings, unadjusted Langevin processes, and adaptive Metropolis-Hastings.
title Weak convergence of adaptive Markov chain Monte Carlo
topic Statistics Theory
60J05, 60J22,
url https://arxiv.org/abs/2406.00820