Saved in:
Bibliographic Details
Main Authors: Roberts, Gareth O., Rosenthal, Jeffrey S.
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2501.16506
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866910802282283008
author Roberts, Gareth O.
Rosenthal, Jeffrey S.
author_facet Roberts, Gareth O.
Rosenthal, Jeffrey S.
contents We investigate the increase in efficiency of simulated and parallel tempering MCMC algorithms when using non-reversible updates to give them "momentum". By making a connection to a certain simple discrete Markov chain, we show that, under appropriate assumptions, the non-reversible algorithms still exhibit diffusive behaviour, just on a different time scale. We use this to argue that the optimally scaled versions of the non-reversible algorithms are indeed more efficient than the optimally scaled versions of their traditional reversible counterparts, but only by a modest speed-up factor of about 42%.
format Preprint
id arxiv_https___arxiv_org_abs_2501_16506
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Quantifying the Speed-Up from Non-Reversibility in MCMC Tempering Algorithms
Roberts, Gareth O.
Rosenthal, Jeffrey S.
Statistics Theory
We investigate the increase in efficiency of simulated and parallel tempering MCMC algorithms when using non-reversible updates to give them "momentum". By making a connection to a certain simple discrete Markov chain, we show that, under appropriate assumptions, the non-reversible algorithms still exhibit diffusive behaviour, just on a different time scale. We use this to argue that the optimally scaled versions of the non-reversible algorithms are indeed more efficient than the optimally scaled versions of their traditional reversible counterparts, but only by a modest speed-up factor of about 42%.
title Quantifying the Speed-Up from Non-Reversibility in MCMC Tempering Algorithms
topic Statistics Theory
url https://arxiv.org/abs/2501.16506