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Utrecht University Repository
Article . 2003
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Universiteit van Amsterdam (UvA) Institutional Repository UvA-DARE
Conference object . 2003
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https://doi.org/10.1063/1.1632...
Article . 2003 . Peer-reviewed
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https://dx.doi.org/10.1063/1.1...
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Biased Monte Carlo Methods

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Other literature type 01 Jan 2003 Netherlands Publisher:AIPJournal:AIP Conference Proceedings, volume 690, pages 99-109 (issn: 0094-243X, Copyright policy )
Authors: Frenkel, D.;

doi: 10.1063/1.1632121

handle: 11245/1.213173

Biased Monte Carlo Methods

Abstract

Polymer simulations make extensive use of biased Monte Carlo schemes. In this paper, I describe a subset of polymer‐simulation algorithms that aim to increase the sampling efficiency by biasing the selection of trial moves. Algorithms that belong to this category are the Configurational Bias MC method (CBMC), Dynamical Pruned Enriched Rosenbluth sampling (DPERM) and Recoil‐Growth (RG) sampling.

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    popularity
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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green
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Netherlands Research Portal