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首先明确两点：

  1. Jensen’s Inequality is the property of convex functions. Convex function 本身就是通过 Jensen’s Inequality 定义的，它们基本就是一回事
  2. 在不同的领域，对 Jensen’s Inequality 做不同的展开，可以得到该特定领域的新的 inequality；所以说 Jensen’s Inequality 可以看做是一个总纲
    • 基本套路是：该领域有一个 convex function，按 Jensen’s Inequality 展开，得到领域内概念 A 小于概念 B

1. Convex Function / Jensen’s Inequality

Let $X$ be a convex set in a real vector space and let $f: X \rightarrow {\mathbb{R}}$ be a function.

  • Definition of convex functions
    • $f$ is called convex if $f$ statisfies Jensen’s Inequality.
  • Jensen’s Inequality
    • $\forall x_{1}, x_{2} \in X, \forall t \in [0,1] :\qquad f(t x_{1} + (1-t) x_{2}) \leq t f(x_{1}) + (1-t) f(x_{2})$
  • Definition of concave functions
    • $f$ is said to be concave if $−f$ is convex.

2. Jensen’s Inequality on Expectations

If $X$ is a random variable and $f$ is a convex function:

\[f(p_1 x_1 + p_2 x_2 + \dots + p_n x_n) \leq p_1 f(x_1) + p_2 f(x_2) + \dots + p_n f(x_n)\]

LHS is essentially $f(\mathrm{E}(X))$ and RHS $\mathrm{E}(f(X))$, which together give

\[f(\mathrm{E}(X)) \leq \mathrm{E}(f(X))\]

3. Gibbs’ Inequality

Let $p = \lbrace p_1, p_2, \dots, p_n \rbrace$ be the true probability distribution for $X$ and $q = \lbrace q_1, q_2, \dots, q_n \rbrace$ be another probability distribution (你可以认为一个假设的 $X$ distrbution). Construct a random variable $Y$ who follows $Y(x) = \frac{q(x)}{p(x)}$. Given $f(y) = -\log(y)$ is a convex function, we have:

\[f(\mathrm{E}(Y)) \leq \mathrm{E}(f(Y))\]

Therefore:

\[\begin{aligned} -\log \sum_{i} \big ( p_i \frac{q_i}{p_i} \big ) & \leq \sum_{i} p_i \big (-\log \frac{q_i}{p_i} \big ) \newline -\log 1 & \leq \sum_{i} p_i \log \frac{p_i}{q_i} \newline 0 & \leq \sum_{i} p_i \log \frac{p_i}{q_i} \end{aligned}\]

如果我们用的是 $\log_2$ 的话，可以称 RHS 为 Kullback–Leibler divergence or relative entropy of $p$ with respect to $q$：

\[D_{KL}(p \Vert q) \equiv \sum_{i} p_i \log_2 \frac{p_i}{q_i} \geq 0\]

这个式子我们称为 Gibbs’ Inequality.

我们接着变形：

\[\begin{aligned} D_{KL}(p \Vert q) \equiv \sum_{i} p_i \log_2 \frac{p_i}{q_i} &= \sum_{i} p_i \log_2 p_i - \sum_{i} p_i \log_2 q_i \newline &= -H(p) + H(p, q) \geq 0 \end{aligned}\]
  • $H(p)$ is the entropy of distribution $p$
  • $H(p, q)$ is the cross entropy of distributions $p$ and $q$
  • $H(p) \leq H(p, q) \Rightarrow$ the information entropy of a distribution $p$ is less than or equal to its cross entropy with any other distribution $q$

Interpretations of $D_{KL}(p \Vert q)$

  • In the context of machine learning, $D_{KL}(p \Vert q)$ is often called the information gain achieved if $q$ is used instead of $p$ (This is why it’s also called the relative entropy of $p$ with respect to $q$).
  • In the context of coding theory, $D_{KL}(p \Vert q)$ can be construed as measuring the expected number of extra bits required to code samples from $p$ using a code optimized for $q$ rather than the code optimized for $p$.
  • In the context of Bayesian inference, $D_{KL}(p \Vert q)$ is amount of information lost when $q$ is used to approximate $p$.
  • 简单说，$D_{KL}(p \Vert q)$ 可以衡量两个 distribution $p$ 和 $q$ 的 “接近程度”
    • 如果 $p=q$，那么 $D_{KL}(p \Vert q) = 0$
    • $p$ 和 $q$ 差异越大，$D_{KL}(p \Vert q)$ 越大

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