用预条件蒙特卡罗加速天文学和宇宙学推论
Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo
摘要：
我们介绍了预条件蒙特卡罗（PMC），一种新的蒙特卡罗方法的贝叶斯推断，有利于有效的抽样概率分布与非平凡的几何形状。PMC利用一个归一化流（NF），以解相关的分布的参数，然后进行采样从预处理的目标分布使用自适应顺序蒙特卡罗（SMC）计划。PMC产生的结果包括来自后验分布的样本和模型证据的估计，可分别用于参数推断和模型比较。上述框架已在各种具有挑战性的目标分布中进行了彻底测试，实现了最先进的采样性能。在原始特征分析和引力波推断的情况下，PMC分别比嵌套采样（NS）快约50倍和25倍。我们发现，在高维应用中，加速度甚至更大。最后，PMC是直接可并行的，表现为线性扩展到数千个CPU。
英文摘要：
We introduce Preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial geometry. PMC utilises a Normalising Flow (NF) in order to decorrelate the parameters of the distribution and then proceeds by sampling from the preconditioned target distribution using an adaptive Sequential Monte Carlo (SMC) scheme. The results produced by PMC include samples from the posterior distribution and an estimate of the model evidence that can be used for parameter inference and model comparison respectively. The aforementioned framework has been thoroughly tested in a variety of challenging target distributions achieving state-of-the-art sampling performance. In the cases of primordial feature analysis and gravitational wave inference, PMC is approximately 50 and 25 times faster respectively than Nested Sampling (NS). We found that in higher dimensional applications the acceleration is even greater. Finally, PMC is directly parallelisable, manifesting linear scaling up to thousands of CPUs.
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