A novel Bayesian reconstruction of the configurational density of states

In this work, we present the development and implementation of a novel Bayesian method for the reconstruction of the density of states (DOS) of a system using energy data obtained from Monte Carlo simulations. This method uses a trial family of functions with adjustable parameters, which are optimized using the Bayes theorem. The measurements can be done in any ensemble with a known distribution function, which significantly helps to overcome energy traps and explore the conformation space thoroughly. We apply our algorithm on a test Potts model system and find that our implementation can find the correct DOS in a reasonable amount of time. Moreover, if the trial function is suitable enough, the DOS found by the algorithm is very close to the actual DOS.