Metropolis Monte Carlo sampling: convergence, localization transition and optimality

Among random sampling methods, Markov chain Monte Carlo (MC) algorithms are foremost. Using a combination of analytical and numerical approaches, we study their convergence properties toward the steady state, within a random walk Metropolis scheme. Analyzing the relaxation properties of some model a...

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Bibliographic Details
Published in:Journal of statistical mechanics 2023-12, Vol.2023 (12), p.123205
Main Authors: Chepelianskii, Alexei D, Majumdar, Satya N, Schawe, Hendrik, Trizac, Emmanuel
Format: Article
Language:English
Subjects:
analysis of algorithms
Brownian motion
classical Monte Carlo simulations
Condensed Matter
Mesoscopic Systems and Quantum Hall Effect
mixing
Physics
sampling algorithms
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Summary:Among random sampling methods, Markov chain Monte Carlo (MC) algorithms are foremost. Using a combination of analytical and numerical approaches, we study their convergence properties toward the steady state, within a random walk Metropolis scheme. Analyzing the relaxation properties of some model algorithms sufficiently simple to enable analytic progress, we show that the deviations from the target steady-state distribution can feature a localization transition as a function of the characteristic length of the attempted jumps defining the random walk. While the iteration of the MC algorithm converges to equilibrium for all choices of jump parameters, the localization transition changes drastically the asymptotic shape of the difference between the probability distribution reached after a finite number of steps of the algorithm and the target equilibrium distribution. We argue that the relaxation before and after the localization transition is respectively limited by diffusion and rejection rates.
ISSN:1742-5468
1742-5468
DOI:10.1088/1742-5468/ad002d