The Wang-Landau algorithm reaches the flat histogram criterion in finite time
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Publication:2443184
DOI10.1214/12-AAP913zbMath1288.65005arXiv1110.4025OpenAlexW3098005430MaRDI QIDQ2443184
Pierre E. Jacob, Robin J. Ryder
Publication date: 4 April 2014
Published in: The Annals of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1110.4025
Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05) Numerical analysis or methods applied to Markov chains (65C40)
Related Items (9)
Warp Bridge Sampling: The Next Generation ⋮ Convergence and efficiency of adaptive importance sampling techniques with partial biasing ⋮ Estimating the volume of the solution space of SMT(LIA) constraints by a flat histogram method ⋮ Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities ⋮ Multicanonical MCMC for sampling rare events: an illustrative review ⋮ Self-healing umbrella sampling: convergence and efficiency ⋮ Bayesian computation: a summary of the current state, and samples backwards and forwards ⋮ Wang-Landau algorithm: an adapted random walk to boost convergence ⋮ Convergence of the Wang-Landau algorithm
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