.png){kind=icon}
返回
.png){kind=icon}
.png){kind=icon}
摘要
En 中文
Nested sampling (NS) computes parameter posterior distribu-tions and makes Bayesian model comparison computationally feasible. Its strengths are the unsupervised navigation of complex, potentially multi -modal posteriors until a well-defined termination point. A systematic liter-ature review of nested sampling algorithms and variants is presented. We focus on complete algorithms, including solutions to likelihood-restricted prior sampling, parallelisation, termination and diagnostics. The relation between number of live points, dimensionality and computational cost is studied for two complete algorithms. A new formulation of NS is presented, which casts the parameter space exploration as a search on a tree data structure. Previously published ways of obtaining robust error estimates and dynamic variations of the number of live points are presented as special cases of this formulation. A new online diagnostic test is presented based on previous insertion rank order work. The survey of nested sampling methods concludes with outlooks for future research.
Keyword:
MONTE-CARLO
MARGINAL LIKELIHOOD
EFFICIENT
COMPUTATION
期刊
S
IF:
15.4
论文数:
30
被引数:
1.1K
机构
引用论文
.png){kind=icon}
DYNESTY: a dynamic nested sampling package for estimating Bayesian posteriors and evidencesDYNESTY: 用于估计贝叶斯后验和证据的动态嵌套抽样包.png)
OAAI
OAAI.png){kind=icon}
分享.png){kind=icon}
收藏
查看更多内容