Wang-Landau algorithm: an adapted random walk to boost convergence
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Publication:777536
DOI10.1016/j.jcp.2020.109366zbMath1436.65005OpenAlexW2923551037MaRDI QIDQ777536
Frédéric Cazals, Augustin Chevallier
Publication date: 7 July 2020
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2020.109366
Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40) Monte Carlo methods applied to problems in statistical mechanics (82M31)
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- Markov chains and stochastic stability
- A note on Metropolis-Hastings kernels for general state spaces
- Optimal scaling for various Metropolis-Hastings algorithms.
- A \(1/t\) algorithm with the density of two states for estimating multidimensional integrals
- Generalized darting Monte Carlo
- The Wang-Landau algorithm reaches the flat histogram criterion in finite time
- Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
- Monte Carlo Simulations in Statistical Physics — From Basic Principles to Advanced Applications
- Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
- Convergence of the Wang-Landau algorithm
- Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
- Monte Carlo sampling methods using Markov chains and their applications