Heat bath efficiency with a Metropolis-type updating

2005/03/31 by Alexei Bazavov, Bernd A. Berg · 3 citations
Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Bayesian probability #Computer science #Geology #Lattice (music) #Markov chain Monte Carlo #Mathematical analysis #Mathematics #Metropolis–Hastings algorithm #Particle physics theoretical and experimental studies #Physics #Quantum Chromodynamics and Particle Interactions #Sampling (signal processing) #Scheme (mathematics) #Statistical physics #Theoretical and Computational Physics #Type (biology) #cond-mat.stat-mech #hep-lat #physics.comp-ph

paper · pdf · doi:10.1103/physrevd.71.114506

published as Phys.Rev. D71 (2005) 114506 · 5 pages, 4 figures. Revisions after referee reports

arxiv created 2005/04/26 · openalex publication_date 2005/06/17 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

We illustrate for 4D SU(2) and U(1) lattice gauge theory that sampling with a biased Metropolis scheme is essentially equivalent to using the heat bath algorithm. Only, the biased Metropolis method can also be applied when an efficient heat bath algorithm does not exist. For the examples discussed the biased Metropolis algorithm is also better suited for parallelization than the heat bath algorithms.

Citations

• Metropolis Importance Sampling for Rugged Dynamical Variables

Cited by

• Biased Metropolis-heat-bath algorithm for fundamental-adjoint SU(2) lattice gauge theory
• Nonperturbative U(1) gauge theory at finite temperature
• Diagrammatic Monte Carlo for the weak-coupling expansion of non-Abelian lattice field theories: Large-N U(N)×U(N) principal chiral model