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arXiv 1202.4808astro-ph.COphysics.data-an
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交叉统计:贝叶斯解释、模型选择和解决暗能量参数化问题

Crossing Statistic: Bayesian interpretation, model selection and resolving dark energy parametrization problem

Arman Shafieloo

摘要:

通过引入交叉函数和超参数,我证明了交叉统计量的贝叶斯解释[1]可以平凡地用于宇宙学模型中的模型选择。在这种证伪宇宙学模型的方法中,不需要将它与其他模型进行比较,也不需要假设任何特定形式的宇宙学量的参数化,如光度距离、哈勃参数或暗能量的状态方程。相反,交叉函数的超参数作为正确和错误模型之间的鉴别器。使用这种方法,人们可以伪造任何假设的宇宙学模型,而不把先验知识放在宇宙的基本实际模型及其参数上,从而解决了暗能量参数化的问题。它也将被证明,该方法的内在分散的数据的敏感性是小的,这是另一个重要的特点,该方法在测试宇宙学模型处理具有高不确定性的数据。

英文摘要:

By introducing Crossing functions and hyper-parameters I show that the Bayesian interpretation of the Crossing Statistics [1] can be used trivially for the purpose of model selection among cosmological models. In this approach to falsify a cosmological model there is no need to compare it with other models or assume any particular form of parametrization for the cosmological quantities like luminosity distance, Hubble parameter or equation of state of dark energy. Instead, hyper-parameters of Crossing functions perform as discriminators between correct and wrong models. Using this approach one can falsify any assumed cosmological model without putting priors on the underlying actual model of the universe and its parameters, hence the issue of dark energy parametrization is resolved. It will be also shown that the sensitivity of the method to the intrinsic dispersion of the data is small that is another important characteristic of the method in testing cosmological models dealing with data with high uncertainties.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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