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 effeciency by biasing the selection of trial moves. Algorithms that belong to this category are the Configurarional Bias MC method (CBMC), Dynamical Pruned Enriched Rosenbluth sampling (DPERM) and Recoil-Growth (RG) sampling.

Additional Metadata
Publisher American Institute of Physics
Editor J. E. Gubernatis
Citation
Frenkel, D. (2003). Biased Monte Carlo methods. In J. E Gubernatis (Ed.), The Monte Carlo method in the physical sciences: Celebrating the 50th anniversary of the metropolis algorithm, Los Alamos, New Mexico, June 9-11, 2003 (pp. 99–109). American Institute of Physics.