论文标题
用非高斯拟合来增强蒙特卡洛抽样
Boosting MonteCarlo sampling with a non-Gaussian fit
论文作者
论文摘要
我们提出了一种称为Montecarlo后拟合的新方法，以提高似然（后）功能的Montecarlo采样。这个想法是通过分析多维非高斯拟合来近似后函数。该拟合的许多自由参数可以通过较小的采样来获得，而不是推导完整数值后部所需的。在基于超新星和宇宙微波背景数据的示例中，我们发现一个数量级要比标准算法中的采样级小，才能达到可比的精度。该方法可以应用于各种情况，预计在所有抽样非常耗时的情况下，将显着提高蒙特卡洛例程的性能。最后，它也可以应用于Fisher矩阵预测，并可以帮助解决标准方法的各种局限性。
We propose a new method, called MonteCarlo Posterior Fit, to boost the MonteCarlo sampling of likelihood (posterior) functions. The idea is to approximate the posterior function by an analytical multidimensional non-Gaussian fit. The many free parameters of this fit can be obtained by a smaller sampling than is needed to derive the full numerical posterior. In the examples that we consider, based on supernovae and cosmic microwave background data, we find that one needs an order of magnitude smaller sampling than in the standard algorithms to achieve comparable precision. This method can be applied to a variety of situations and is expected to significantly improve the performance of the MonteCarlo routines in all the cases in which sampling is very time-consuming. Finally, it can also be applied to Fisher matrix forecasts, and can help solve various limitations of the standard approach.
