Estimating MCMC convergence rates using common random number simulation

Fuente: arXiv
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Main Authors: Sixta, Sabrina, Rosenthal, Jeffrey S., Brown, Austin
Format: Preprint
Published: 2023
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author Sixta, Sabrina
Rosenthal, Jeffrey S.
Brown, Austin
author_facet Sixta, Sabrina
Rosenthal, Jeffrey S.
Brown, Austin
contents This paper presents how to use common random number (CRN) simulation to evaluate Markov chain Monte Carlo (MCMC) convergence to stationarity. We provide an upper bound on the Wasserstein distance of a Markov chain to its stationary distribution after $N$ steps in terms of averages over CRN simulations. We apply our bound to Gibbs samplers on a model related to James-Stein estimators, a variance component model, and a Bayesian linear regression model. For the first two examples, we show that the CRN simulated bound converges to zero significantly more quickly compared to available drift and minorization bounds.
format Preprint
id arxiv_https___arxiv_org_abs_2309_15735
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Estimating MCMC convergence rates using common random number simulation
Sixta, Sabrina
Rosenthal, Jeffrey S.
Brown, Austin
Computation
This paper presents how to use common random number (CRN) simulation to evaluate Markov chain Monte Carlo (MCMC) convergence to stationarity. We provide an upper bound on the Wasserstein distance of a Markov chain to its stationary distribution after $N$ steps in terms of averages over CRN simulations. We apply our bound to Gibbs samplers on a model related to James-Stein estimators, a variance component model, and a Bayesian linear regression model. For the first two examples, we show that the CRN simulated bound converges to zero significantly more quickly compared to available drift and minorization bounds.
title Estimating MCMC convergence rates using common random number simulation
topic Computation
url https://arxiv.org/abs/2309.15735