Name
Abstract | ||
|---|---|---|
We analyze the convergence properties of the Wang-Landau algorithm. This sampling method belongs to the general class of adaptive importance sampling strategies which use the free energy along a chosen reaction coordinate as a bias. Such algorithms are very helpful to enhance the sampling properties of Markov Chain Monte Carlo algorithms, when the dynamics is metastable. We prove the convergence of the Wang-Landau algorithm and an associated central limit theorem. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1090/S0025-5718-2015-02952-4 | MATHEMATICS OF COMPUTATION |
Field | DocType | Volume |
Slice sampling,Convergence (routing),Rejection sampling,Umbrella sampling,Combinatorics,Importance sampling,Markov chain Monte Carlo,Compact convergence,Algorithm,Sampling (statistics),Mathematics | Journal | 84 |
Issue | ISSN | Citations |
295 | 0025-5718 | 3 |
PageRank | References | Authors |
0.47 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gersende Fort | 1 | 150 | 16.59 |
| Benjamin Jourdain | 2 | 5 | 1.36 |
| Estelle Kuhn | 3 | 10 | 1.59 |
| Tony Lelièvre | 4 | 33 | 9.48 |
| Gabriel Stoltz | 5 | 23 | 8.85 |