A nested sampling algorithm for cosmological model selection
A nested sampling algorithm for cosmological model selection
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DOI：
10.1086/501068
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发表时间：
2006-02-20
影响因子：
4.9
通讯作者：
Liddle, AR
中科院分区：
文献类型：
--
作者：
Mukherjee, P;Parkinson, D;Liddle, AR
宇宙数据的丰富性意味着可以测试的宇宙模型比以往任何时候都要广泛。然而，参数拟合和模型选择之间必须有一个重要的区别。虽然参数拟合仅仅决定了模型与数据的拟合程度，但模型选择统计，如贝叶斯证据，现在需要在这些不同的模型之间进行选择，特别是评估对新参数的需求。我们实现了一种新的证据算法，即嵌套抽样，它结合了准确性、通用性和计算可行性，并将其应用于一些宇宙学数据集和模型。我们发现具有Harrison-Zel'dovich初始谱的五参数模型目前是首选的。
The abundance of cosmological data becoming available means that a wider range of cosmological models are testable than ever before. However, an important distinction must be made between parameter fitting and model selection. While parameter fitting simply determines how well a model fits the data, model selection statistics, such as the Bayesian evidence, are now necessary to choose between these different models, and in particular to assess the need for new parameters. We implement a new evidence algorithm known as nested sampling, which combines accuracy, generality of application, and computational feasibility, and we apply it to some cosmological data sets and models. We find that a five-parameter model with a Harrison-Zel'dovich initial spectrum is currently preferred.