論文

査読有り
2017年12月

Distance between configurations in Markov chain Monte Carlo simulations

JOURNAL OF HIGH ENERGY PHYSICS
  • Masafumi Fukuma
  • ,
  • Nobuyuki Matsumoto
  • ,
  • Naoya Umeda

12
12
開始ページ
001
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1007/JHEP12(2017)001
出版者・発行元
SPRINGER

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 local moves in the configuration space. We explicitly calculate the distance for the Langevin algorithm, and show that it certainly has desired and expected properties as distance. We further show that the distance for a multimodal distribution gets dramatically reduced from a large value by the introduction of a tempering method. We also argue that, when the original distribution is highly multimodal with large number of degenerate vacua, an anti-de Sitter-like geometry naturally emerges in the extended configuration space.

Web of Science ® 被引用回数 : 2

リンク情報
DOI
https://doi.org/10.1007/JHEP12(2017)001
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000417756100001&DestApp=WOS_CPL
URL
http://orcid.org/0000-0001-8146-5034

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