论文标题

$ q $ - 高斯措施的熵自由度

Gauge freedom of entropies on $q$-Gaussian measures

论文作者

Matsuzoe, Hiroshi, Takatsu, Asuka

论文摘要

$ q $ - 高斯度量是高斯措施的概括。通过用指数函数替换指数函数$ 1/(1-q)$($ q \ neq 1 $)来获得此概括。极限情况$ q = 1 $恢复了高斯措施。对于$ 1 \ leq q <3 $,实际线上的所有$ q $ -Gaussian密度的集合满足了通过护送期望来定义信息几何结构(例如熵和相对熵)的某些规律性条件。 The ordinary expectation of a random variable is the integral of the random variable with respect to its law.陪同期望我们承认我们将法律替换为其他任何措施。在所有$ Q $ -Gaussian密度的集合中选择的护送期望决定了熵和相对熵。所有$ Q $ -Gaussian密度的最重要的护送期望之一是$ Q $ -ESCORT期望,因为此护送期望决定了Tsallis熵和Tsallis相对熵。 熵的现象量规是,不同的护送期望决定了相同的熵,但相对熵不同。在本说明中,我们首先介绍了$ q $ logarithmic函数的改进。然后,我们通过使用精制的$ q $ logarithmic函数在真实线上的所有$ q $ -Gaussian密度上的公开集中演示了这种现象。我们写下相应的Riemannian指标。

A $q$-Gaussian measure is a generalization of a Gaussian measure. This generalization is obtained by replacing the exponential function with the power function of exponent $1/(1-q)$ ($q\neq 1$). The limit case $q=1$ recovers a Gaussian measure. For $1\leq q <3$, the set of all $q$-Gaussian densities over the real line satisfies a certain regularity condition to define information geometric structures such as an entropy and a relative entropy via escort expectations. The ordinary expectation of a random variable is the integral of the random variable with respect to its law. Escort expectations admit us to replace the law to any other measures. A choice of escort expectations on the set of all $q$-Gaussian densities determines an entropy and a relative entropy. One of most important escort expectations on the set of all $q$-Gaussian densities is the $q$-escort expectation since this escort expectation determines the Tsallis entropy and the Tsallis relative entropy. The phenomenon gauge freedom of entropies is that different escort expectations determine the same entropy, but different relative entropies. In this note, we first introduce a refinement of the $q$-logarithmic function. Then we demonstrate the phenomenon on an open set of all $q$-Gaussian densities over the real line by using the refined $q$-logarithmic functions. We write down the corresponding Riemannian metric.

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