Convergence of the Wang-Landau algorithm
  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. 
Gersende Fort, Benjamin Jourdain, Estelle Kuhn, Tony Leli\`evre,   Gabriel Stoltz
math.PR math.ST stat.TH