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Umbrella sampling is a method used within Markov chain Monte Carlo (MCMC) to chose the values to evaluate, that avoids some issues that can occur with the more-usual, earlier-developed Metropolis algorithm and its variants. The Metropolis algorithm does not sufficiently cover the parameter space of some functions, and Umbrella sampling is specifically designed to overcome this.