A simple modification of the "Wang-Landau sampling" algorithm removes the systematic error that occurs at the boundary of the range of energy over which the random walk takes place in the original algorithm. In two recent papers [1, 2] an efficient Monte Carlo procedure was introduced that used a random walk in energy space to obtain an accurate estimate of the energy density of states g(E) for classical statistical models. If this method (now commonly termed "Wang-Landau Sampling") is applied to a restricted energy range, effects at the boundaries of the energy range come into play, and systematically larger errors in g(E) at the edges of the sampled energy interval are observed. Since the method is of quite general applicability, a better understanding of these "edge" effects could be of considerable value. Here, we show how such an enhancement of errors at the edges can be avoided by a simple modification of the algorithm. In Wang-Landau Sampling one accepts trial configurations with probability min(1, g(E)/g(E ′ )), where g(E) is the energy density of states (DOS) and E and E ′ are the energies of the current and the proposed configuration, respectively.
Wang-Landau sampling REF ] is a popular MCMC sampling method in statistical physics that involves a random walk in the energy space rather than sampling at a fixed temperature.