article · Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields · 2000
A noisy Monte Carlo algorithm
Cited by 43Open access
Abstract
We propose a Monte Carlo algorithm to promote the Kennedy-Kuti linear accept-reject algorithm which accommodates unbiased stochastic estimates of the probability to an exact one. This is achieved by adopting the Metropolis accept-reject steps for both the dynamical and noise configurations. We test it on the five state model and obtain desirable results even for the case with large noise. We also discuss its application to lattice QCD with stochastically estimated fermion determinants.
Cite this paper
Lin, L., Liu, K. F., & Sloan, J. (2000). A noisy Monte Carlo algorithm. Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields, 61(7). https://doi.org/10.1103/physrevd.61.074505
Lin, Leyu, et al. “A noisy Monte Carlo algorithm.” Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields, vol. 61, no. 7, 2000, https://doi.org/10.1103/physrevd.61.074505.
Lin, Leyu, K. F. Liu, and J. Sloan. 2000. “A noisy Monte Carlo algorithm.” Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields 61 (7). https://doi.org/10.1103/physrevd.61.074505.
Lin, L., Liu, K.F. and Sloan, J. (2000) ‘A noisy Monte Carlo algorithm’, Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields, 61(7). Available at: https://doi.org/10.1103/physrevd.61.074505.
@article{lin2000noisy,
title = {{A noisy Monte Carlo algorithm}},
author = {Lin, Leyu and Liu, K. F. and Sloan, J.},
journal = {Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields},
volume = {61},
number = {7},
year = {2000},
doi = {10.1103/physrevd.61.074505}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.