Gaussbock：贝叶斯非参数的快速并行迭代宇宙学参数估计
Gaussbock: Fast parallel-iterative cosmological parameter estimation with Bayesian nonparametrics
摘要：
我们提出并应用Gaussbock，一个新的并行迭代算法宇宙学参数估计设计的廉价的并行计算资源的时代。Gaussbock使用贝叶斯非参数和截断重要性抽样准确地从后验分布中提取样本，与其他方法相比，壁时间的数量级加速。在这方面的当代问题往往遭受由于高维参数空间和随之而来的过多的时间要求，以及需要微调的建议分布或采样参数的计算成本增加。Gaussbock是专门针对这些问题而设计的。我们探索和验证的性能和收敛性的算法上的快速近似的暗能量调查1年（DES Y1）后，发现合理的缩放行为与参数的数量。然后，我们使用大规模超级计算设施对完整的DES Y1后验进行测试，并恢复与以前的链的合理一致性，尽管该算法可能低估了约束不良参数的尾部。此外，我们讨论和演示高斯博克如何恢复复杂的后验形状很好地在较低的维度，但面临的挑战，在更高的维度上这样的分布表现良好。此外，我们提供了一个用户友好的软件工具，加速宇宙学参数估计的基础上本文所述的方法。
英文摘要：
We present and apply Gaussbock, a new embarrassingly parallel iterative algorithm for cosmological parameter estimation designed for an era of cheap parallel computing resources. Gaussbock uses Bayesian nonparametrics and truncated importance sampling to accurately draw samples from posterior distributions with an orders-of-magnitude speed-up in wall time over alternative methods. Contemporary problems in this area often suffer from both increased computational costs due to high-dimensional parameter spaces and consequent excessive time requirements, as well as the need for fine tuning of proposal distributions or sampling parameters. Gaussbock is designed specifically with these issues in mind. We explore and validate the performance and convergence of the algorithm on a fast approximation to the Dark Energy Survey Year 1 (DES Y1) posterior, finding reasonable scaling behavior with the number of parameters. We then test on the full DES Y1 posterior using large-scale supercomputing facilities, and recover reasonable agreement with previous chains, although the algorithm can underestimate the tails of poorly-constrained parameters. Additionally, we discuss and demonstrate how Gaussbock recovers complex posterior shapes very well at lower dimensions, but faces challenges to perform well on such distributions in higher dimensions. In addition, we provide the community with a user-friendly software tool for accelerated cosmological parameter estimation based on the methodology described in this paper.
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