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A mutual information based distance for multivariate Gaussian processes

机译:基于相互信息的多元高斯过程的距离

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摘要

In this paper a distance on the set of multivariate Gaussian linear stochastic processes is proposed based on the concept of mutual information. The definition of the distance is inspired by various properties of the mutual information of past and future of a stochastic process. For two special classes of models a link exists between this mutual information distance and a previously defined scalar cepstral distance. Finally, it is demonstrated that the distance shows similar behavior to an ad hoc defined multivariate cepstral distance.
机译:在本文中,基于相互信息的概念提出了一组多变量高斯线性随机流程的距离。距离的定义受到过去和随机过程的过去和未来相互信息的各种性质的启发。对于两种特殊的模型,在该相互信息距离和先前定义的标量距离之间存在链接。最后,证明距离显示出与临时定义多变量倒距距距离的类似行为。

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