The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree determined by the parameter $\lambda$, with the other distribution. Such divergence is an approximation of the KL divergence that does not require the target distribution to be absolutely continuous with respect to the source distribution. In this paper, an information geometric generalization of the skew divergence called the $\alpha$-geodesical skew divergence is proposed, and its properties are studied.

中文翻译：

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α-大地偏斜散度

非对称歪斜散度通过将分布之一混合到由参数确定的程度来平滑分布 $\lambda$，另配。这种散度是KL散度的近似值，KL散度不需要目标分布相对于源分布是绝对连续的。在本文中，偏度散度的信息几何一般化称为$\alpha$提出了大地偏斜散度，并对其性质进行了研究。