论文标题
动态高斯牛顿大都会算法
Dynamic Gauss Newton Metropolis Algorithm
论文作者
论文摘要
GNM：MCMC Jagger。一个令人敬畏的采样器。这个Python软件包是基于动态高斯 - 纽顿 - 摩托波利斯（GNM）算法的仿射不变的马尔可夫链蒙特卡洛（MCMC）采样器。 GNM算法专门针对表单$ e^{ - || f（x）||^2/2} $的高度非线性后验概率分布函数进行采样，并且该软件包是该算法的实现。除了原始GNM算法中的后退策略之外，还添加了动态的超参数优化功能，并包含在软件包中，以帮助提高后退的性能，从而提高采样。此外，还有Jacobian测试仪，错误栏的创建者和更多功能，以便于代码中包含的易用性。引入了问题，并在介绍中给出了安装指南。然后解释了如何使用Python软件包。给出了该算法，最后有一些示例使用指数时间序列来显示算法的性能和后退策略。
GNM: The MCMC Jagger. A rocking awesome sampler. This python package is an affine invariant Markov chain Monte Carlo (MCMC) sampler based on the dynamic Gauss-Newton-Metropolis (GNM) algorithm. The GNM algorithm is specialized in sampling highly non-linear posterior probability distribution functions of the form $e^{-||f(x)||^2/2}$, and the package is an implementation of this algorithm. On top of the back-off strategy in the original GNM algorithm, there is the dynamic hyper-parameter optimization feature added to the algorithm and included in the package to help increase performance of the back-off and therefore the sampling. Also, there are the Jacobian tester, error bars creator and many more features for the ease of use included in the code. The problem is introduced and a guide to installation is given in the introduction. Then how to use the python package is explained. The algorithm is given and finally there are some examples using exponential time series to show the performance of the algorithm and the back-off strategy.
