用高斯过程重建概率分布
Reconstructing Probability Distributions with Gaussian Processes
摘要：
现代宇宙学分析使用马尔可夫链蒙特卡罗（MCMC）或类似的采样技术来约束物理参数。通常情况下，这些技术运行起来在计算上是昂贵的，并且需要高达数千个CPU小时来完成。在这里，我们提出了一种从现有的MCMC链（或任何一组后验样本）重建已完成实验的对数概率分布的方法。重建是使用高斯过程回归插值的对数概率。这允许简单的重新排序，重要性采样，边缘化，测试不同的采样器，调查链收敛和其他操作。作为一个示例用例，我们重建了最近的普朗克2018分析的后验分布。然后，我们对后验数据进行重新采样，并在30分钟内生成一个新的MCMC链，其点数是前一个的40倍。我们的可能性重建工具可以在https://github.com/tmcclintock/AReconstructionTool上找到。
英文摘要：
Modern cosmological analyses constrain physical parameters using Markov Chain Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques are computationally expensive to run and require up to thousands of CPU hours to complete. Here we present a method for reconstructing the log-probability distributions of completed experiments from an existing MCMC chain (or any set of posterior samples). The reconstruction is performed using Gaussian process regression for interpolating the log-probability. This allows for easy resampling, importance sampling, marginalization, testing different samplers, investigating chain convergence, and other operations. As an example use-case, we reconstruct the posterior distribution of the most recent Planck 2018 analysis. We then resample the posterior, and generate a new MCMC chain with forty times as many points in only thirty minutes. Our likelihood reconstruction tool can be found online at https://github.com/tmcclintock/AReconstructionTool.
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。
