非高斯随机参数化

P. Dewilde
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摘要

本文讨论了基于高阶矩的多变量非高斯随机变量集的独立参数参数化问题。这个问题与构建低复杂性模型特别相关,这些模型满足变量幂之间测量的相关数据。结果表明,在相关数据按递增程度严格排序的情况下,存在这样的参数化,并简要说明了如何构造这样的参数化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Non-Gaussian Stochastic Parametrization
The paper addresses the question of parametrization with, independent parameters for a multi-variable, non-Gaussian set of stochastic variables, based on higher order moments. The issue is particularly relevant for the construction of low-complexity models that meet measured correlation data between powers of the variables. It turns out that such a parametrization exists in the case where the correlation data is stricty ordered by increasing degree, and the paper shows in outline how it can be constructed.
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