非高斯似然的一种快速、始终正定和可归一化的近似
A fast, always positive definite and normalizable approximation of non-Gaussian likelihoods
摘要：
在本文中，我们扩大以前发表的DALI近似的情况下，参数依赖性是在协方差矩阵的可能性。近似恢复非高斯似然，并减少到高斯的情况下的Fisher矩阵的方法。它的工作原理是对数据具有高斯误差的最小假设，以及具有收敛泰勒近似的协方差矩阵。由此产生的近似工程的情况下，严重的参数退化和Fisher矩阵是奇异的。它至少比在相同参数空间上运行的典型蒙特卡罗马尔可夫链快$1000$倍。两个例子的应用，极端非高斯似然的情况下，提出-一个演示了如何成功地重建完全环形的可能性的方法。这里发布了一个公共代码：http://lnasellentin.github.io/DALI/
英文摘要：
In this paper we extent the previously published DALI-approximation for likelihoods to cases in which the parameter dependency is in the covariance matrix. The approximation recovers non-Gaussian likelihoods, and reduces to the Fisher matrix approach in the case of Gaussianity. It works with the minimal assumptions of having Gaussian errors on the data, and a covariance matrix that possesses a converging Taylor approximation. The resulting approximation works in cases of severe parameter degeneracies and in cases where the Fisher matrix is singular. It is at least $1000$ times faster than a typical Monte Carlo Markov Chain run over the same parameter space. Two example applications, to cases of extremely non-Gaussian likelihoods, are presented -- one demonstrates how the method succeeds in reconstructing completely a ring-shaped likelihood. A public code is released here: http://lnasellentin.github.io/DALI/
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。
