1998/11/30 by Philippe de Forcrand, Ph. de Forcrand · 22 citations
Computer Science · Mathematics · Physics and Astronomy · #Applied mathematics #Distribution (mathematics) #Gaussian #Geology #Image and Signal Denoising Methods #Inversion (geology) #Markov Chains and Monte Carlo Methods #Mathematical Approximation and Integration #Mathematical analysis #Mathematical physics #Mathematics #Monte Carlo method #Physics #Quantum mechanics #Sampling (signal processing) #Statistical physics #Statistics #cond-mat #hep-lat
paper · pdf · doi:10.1103/physreve.59.3698
published in Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics 59(3), 3698-3701 (American Physical Society) · 10 pages, 1 figure; typos corrected, reference added; version to appear in Phys. Rev. E
arxiv created 1999/01/07 · openalex publication_date 1999/03/01 · arxiv updated 2009/11/30 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
When sampling the distribution P(\stackrel\ensuremath→\ensuremathφ)\ensuremath∝exp(\ensuremath-|A\stackrel\ensuremath→\ensuremathφ|2), a global heat bath normally proceeds by solving the linear system A\stackrel\ensuremath→\ensuremathφ=\stackrel\ensuremath→\ensuremathη, where \stackrel\ensuremath→\ensuremathη is a normal Gaussian vector, exactly. This paper shows how to preserve the distribution P(\stackrel\ensuremath→\ensuremathφ) while solving the linear system with arbitrarily low accuracy. Generalizations are presented.