论文标题
部分可观测时空混沌系统的无模型预测
Non-iterative Gaussianization
论文作者
论文摘要
在这项工作中，我们提出了基于副函数的非著作高斯转化策略，该策略不需要在先前的研究中进行一些常见的限制性假设，例如椭圆形的对称分布假设和线性独立的组件分析假设。理论属性保证所提出的策略可以将具有连续多元分布的任何随机变量向量准确地转移到遵循多元高斯分布的变量向量。模拟研究还表明，与盒子高斯式高斯和径向高斯化等其他方法相比，这种策略的表现要出色。还显示了图像合成的概率密度估计的应用。
In this work, we propose a non-iterative Gaussian transformation strategy based on copula function, which doesn't require some commonly seen restrictive assumptions in the previous studies such as the elliptically symmetric distribution assumption and the linear independent component analysis assumption. Theoretical properties guarantee the proposed strategy can exactly transfer any random variable vector with a continuous multivariate distribution to a variable vector that follows a multivariate Gaussian distribution. Simulation studies also demonstrate the outperformance of such a strategy compared to some other methods like Box-Cox Gaussianization and radial Gaussianization. An application for probability density estimation for image synthesis is also shown.
