Towards derandomising Markov chain Monte Carlo

We present a new framework to derandomise certain Markov chain Monte Carlo (MCMC) algorithms. As in MCMC, we first reduce counting problems to sampling from a sequence of marginal distributions. For the latter task, we introduce a method called coupling towards the past that can, in logarithmic time...

Full description

Bibliographic Details

Published in:	Proceedings / annual Symposium on Foundations of Computer Science pp. 1963 - 1990
Main Authors:	Feng, Weiming, Guo, Heng, Wang, Chunyang, Wang, Jiaheng, Yin, Yitong
Format:	Conference Proceeding
Language:	English
Published:	IEEE 06.11.2023
Subjects:	approximate counting Approximation algorithms Computer science Couplings deterministic algorithm Markov chain Monte Carlo Markov processes Monte Carlo methods Task analysis
ISSN:	2575-8454
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!