论文标题
与预处理的蒙特卡洛一起加速天文学和宇宙学推断
Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo
论文作者
论文摘要
我们介绍了预处理的蒙特卡洛（PMC），这是一种用于贝叶斯推断的新型蒙特卡洛方法，可促进具有非平凡几何形状的概率分布的有效抽样。 PMC利用归一流的流（NF）来驱动分布的参数，然后使用自适应顺序蒙特卡洛（SMC）方案从预处理的目标分布中进行采样。 PMC产生的结果包括来自后验分布的样本以及可分别用于参数推理和模型比较的模型证据的估计。上述框架已在实现最新抽样性能的各种具有挑战性的目标分布中进行了彻底的测试。在原始特征分析和重力波推断的情况下，PMC分别比嵌套采样（NS）高约50和25倍。我们发现，在较高维度的应用中，加速度更大。最后，PMC是直接平行的，表现为线性扩展高达数千个CPU。
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.
