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我从许多关于贝叶斯实验设计的论文中看到，Kullback-Leibler 从后验到先验的发散是数据提供的“信息增益”。然而，由于 KL Divergence 总是非负的，有没有办法（或如何）从先验到后验解释 KL Divergence？