Properties of nested sampling'

C. Robert, N. CHOPIN


2010, vol. 97, n°3, pp.741-755

Départements : Economie et Sciences de la décision

Mots clés : Central limit theorem, Evidence, Importance sampling, Marginal likelihood, Markov chain Monte Carlo simulation, Nested sampling

Nested sampling is a simulation method for approximating marginal likelihoods. We establish that nested sampling has an approximation error that vanishes at the standard Monte Carlo rate and that this error is asymptotically Gaussian. It is shown that the asymptotic variance of the nested sampling approximation typically grows linearly with the dimension of the parameter. We discuss the applicability and efficiency of nested sampling in realistic problems, and compare it with two current methods for computing marginal likelihood. Finally, we propose an extension that avoids resorting to Markov chain Monte Carlo simulation to obtain the simulated points