Properties of nested sampling

• 成果类型: Article
• 署名作者: Chopin, Nicolas; Robert, Christian P.
• 署名单位: Institut Polytechnique de Paris; ENSAE Paris; Universite PSL; Universite Paris-Dauphine
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asq021
• 发表日期: 2010
• 页码: 741755
• 关键词: monte-carlo methods; ratios

摘要

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.