Distance between configurations in Markov chain Monte Carlo simulations

Distance between configurations in Markov chain Monte Carlo simulations

Abstract For a given Markov chain Monte Carlo algorithm we introduce a distance between two configurations that quantifies the difficulty of transition from one configuration to the other configuration. We argue that the distance takes a universal form for the class of algorithms which generate loca...

Full description

Bibliographic Details
Main Authors: Masafumi Fukuma, Nobuyuki Matsumoto, Naoya Umeda
Format: Article
Language:English
Published: SpringerOpen 2017-12-01
Series:Journal of High Energy Physics
Subjects:
Random Systems
Stochastic Processes
Lattice QCD
AdS-CFT Correspondence
Online Access:http://link.springer.com/article/10.1007/JHEP12(2017)001

Internet

http://link.springer.com/article/10.1007/JHEP12(2017)001

Similar Items