UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Geometric Ergodicity of the Random Walk Metropolis with Position-Dependent Proposal Covariance

Livingstone, S; (2021) Geometric Ergodicity of the Random Walk Metropolis with Position-Dependent Proposal Covariance. Mathematics , 9 (4) , Article 341. 10.3390/math9040341. Green open access

[thumbnail of mathematics-09-00341.pdf]
Preview
Text
mathematics-09-00341.pdf - Accepted Version

Download (344kB) | Preview

Abstract

Geometric Ergodicity of the Random Walk Metropolis with Position-Dependent Proposal Covariance N (x, hG(x)^{1}), where x is the current state, and study its ergodicity properties. We show that suitable choices of G(x) can change these ergodicity properties compared to the Random Walk Metropolis case N (x, hΣ), either for better or worse. We find that if the proposal variance is allowed to grow unboundedly in the tails of the distribution then geometric ergodicity can be established when the target distribution for the algorithm has tails that are heavier than exponential, in contrast to the Random Walk Metropolis case, but that the growth rate must be carefully controlled to prevent the rejection rate approaching unity. We also illustrate that a judicious choice of G(x) can result in a geometrically ergodic chain when probability concentrates on an ever narrower ridge in the tails, something that is again not true for the Random Walk Metropolis.

Type: Article
Title: Geometric Ergodicity of the Random Walk Metropolis with Position-Dependent Proposal Covariance
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/math9040341
Publisher version: https://doi.org/10.3390/math9040341
Language: English
Additional information: © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Keywords: Monte Carlo; MCMC; Markov chains; computational statistics; bayesian inference
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10121180
Downloads since deposit
92Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item