  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.
