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Non-Gaussian interaction information: estimation, optimization and diagnostic application of triadic wave resonance

机译:非高斯相互作用信息:三重波共振的估计,优化和诊断应用

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摘要

Non-Gaussian multivariate probability distributions, derived from climate and geofluid statistics, allow for nonlinear correlations between linearly uncorrelated components, due to joint Shannon negentropies. Triadic statistical dependence under pair-wise (total or partial) independence is thus possible. Synergy or interaction information among triads is estimated. We formulate an optimization method of triads in the space of orthogonal rotations of normalized principal components, relying on the maximization of thirdorder cross-cumulants. Its application to a minimal onedimensional, periodic, advective model leads to enhanced triads that occur between oscillating components of circular or locally confined wave trains satisfying the triadic wave resonance condition.
机译:从气候和地流体统计数据中得出的非高斯多元概率分布,由于联合香农熵,使得线性不相关成分之间具有非线性相关性。因此,在成对的(全部或部分)独立性下三元组统计依赖性是可能的。估计三合会之间的协同作用或相互作用信息。依靠三阶交叉累积量的最大化,在归一化主分量正交旋转的空间中,制定了三元组的优化方法。将其应用于最小的一维,周期性,对流模型会导致增强的三合会,这些三合会出现在满足三合体波共振条件的圆形或局域约束波列的振动分量之间。

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