Nested sampling is a novel simulation method for approximating marginal likelihoods, proposed by Skilling (2007a,b). We establish that nested sampling leads to an error that vanishes at the standard Monte Carlo rate N−1/2, where N is a tuning parameter that is proportional to the computational effort, and that this error is asymptotically Gaussian. We show that the corresponding asymptotic variance typically grows linearly with the dimension of the parameter. We use these results to discuss the… CONTINUE READING