Nested sampling for Potts models

Nested sampling is a new Monte Carlo method by Skilling [1] intended forgeneral Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement isan ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We provide a demonstration withthe Potts model, an undirected graphical model.