KL(kullback-Leibler-devergence)散度(相对熵)非负性

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KL(kullback-Leibler-devergence)散度(相对熵)非负性证明

两个概率分布:P(X)和Q(X),两个分布之间的距离:

D_{KL}(P||Q)=\sum_{x\in X}P(x)log\frac{P(x)}{Q(x)}=E[log\frac{P(X)}{Q(X)}]=-E[log\frac{Q(X)}{P(X)}]

根据jensen不等式,log函数为上凸函数,则E(f(X))\geq f(E(X))

clip_image019

则进一步化简:\tiny -E(log\frac{Q(X)}{P(X)})\geq -log [E(\frac{P(X)}{Q(X)})]=-log[\sum_{x\in X}P(X)\frac{Q(X)}{P(X)}]=-log(\sum_{x\in X}Q(X))=0

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转载自blog.csdn.net/KID_yuan/article/details/84800434