In systems with a large number of degrees of freedom, like spin systems, Monte Carlo integration is required. In this integration, importance sampling and in particular the Metropolis algorithm, is a very important technique. However, the Metropolis algorithm samples states according to exp ⁡ ⁡ ( − − β β E ) where beta is the inverse of the temperature. This means that an energy barrier of Δ Δ E on the energy spectrum is exponentially difficult to overcome. Systems with multiple local energy minima like the Potts model become hard to sample as the algorithm gets stuck in the system's local minima. This motivates other approaches, namely, other sampling distributions.