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.

翻译：不对称的斜差通过混合来平滑其中的分布, 其程度由参数 $\ lumbda$ 确定, 与其它分布 。 这种差异是 KL 差异的近似值, 并不要求目标分布在源分布上绝对连续。 本文建议对斜差进行信息几何学概括, 称为 $\ alpha$- geodiscal skew 差异, 并研究其属性 。