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Measures of distinguishability between stochastic processes.
Physical Review E ( IF 2.4 ) Pub Date : 2020-06-23 , DOI: 10.1103/physreve.101.062137
Chengran Yang 1, 2 , Felix C Binder 3 , Mile Gu 1, 2, 4 , Thomas J Elliott 1, 2
Affiliation  

Quantifying how distinguishable two stochastic processes are is at the heart of many fields, such as machine learning and quantitative finance. While several measures have been proposed for this task, none have universal applicability and ease of use. In this article, we suggest a set of requirements for a well-behaved measure of process distinguishability. Moreover, we propose a family of measures, called divergence rates, that satisfy all of these requirements. Focusing on a particular member of this family—the coemission divergence rate—we show that it can be computed efficiently, behaves qualitatively similar to other commonly used measures in their regimes of applicability, and remains well behaved in scenarios where other measures break down.

中文翻译:

随机过程之间的可区分性度量。

量化两个随机过程的可分辨性是许多领域的核心,例如机器学习和量化金融。尽管已针对此任务提出了几种措施,但没有一种措施具有普遍适用性和易用性。在本文中,我们提出了一套规范的过程可区分性度量要求。此外,我们提出了一系列满足所有这些要求的措施,称为发散率。着眼于这个家庭的一个特定成员-共发散率,我们表明它可以有效地计算,在适用范围内在质量上与其他常用度量相似,并且在其他度量失效的情况下仍然表现良好。
更新日期:2020-06-23
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