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...

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Veröffentlicht in:	Proceedings / annual Symposium on Foundations of Computer Science S. 1963 - 1990
Hauptverfasser:	Feng, Weiming, Guo, Heng, Wang, Chunyang, Wang, Jiaheng, Yin, Yitong
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 06.11.2023
Schlagworte:	approximate counting Approximation algorithms Computer science Couplings deterministic algorithm Markov chain Monte Carlo Markov processes Monte Carlo methods Task analysis
ISSN:	2575-8454
Online-Zugang:	Volltext
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