Nested sampling for physical scientists
Nested sampling for physical scientists
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DOI：
10.1038/s43586-022-00121-x
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发表时间：
2022-05-26
期刊：
影响因子：
--
通讯作者：
Yallup, David
中科院分区：
文献类型：
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作者：
Ashton, Greg;Bernstein, Noam;Yallup, David
这本入门书研究了Skilling的贝叶斯推理的嵌套抽样算法，更广泛地说，多维整合。嵌套抽样的原则进行了总结和最近的发展，使用高效的嵌套抽样算法在高维度调查，包括抽样方法的限制前。不同的方法应用嵌套抽样概述，详细的例子从三个科学领域：宇宙学，引力波天文学和材料科学。最后，入门包括最佳实践的建议，以及对嵌套采样的潜在限制和优化的讨论。
This Primer examines Skilling's nested sampling algorithm for Bayesian inference and, more broadly, multidimensional integration. The principles of nested sampling are summarized and recent developments using efficient nested sampling algorithms in high dimensions surveyed, including methods for sampling from the constrained prior. Different ways of applying nested sampling are outlined, with detailed examples from three scientific fields: cosmology, gravitational-wave astronomy and materials science. Finally, the Primer includes recommendations for best practices and a discussion of potential limitations and optimizations of nested sampling.