将KL散度转化为度量
KL散度衡量两个随机变量分布的差异，非负且当分布相同时为零，但因不满足对称性和三角不等式而非度量。本文介绍Jeffreys散度（对称化KL散度）和Jensen-Shannon散度（进一步通过取平均分布保证对称性且有界），后者平方根可作为真正的度量，广泛应用于机器学习与信息论。
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