Proof: Non-negativity of the Kullback-Leibler divergence
Index:
The Book of Statistical Proofs ▷
General Theorems ▷
Information theory ▷
Kullback-Leibler divergence ▷
Non-negativity
Metadata: ID: P166 | shortcut: kl-nonneg2 | author: JoramSoch | date: 2020-09-09, 07:02.
Theorem: The Kullback-Leibler divergence is always non-negative
with \mathrm{KL}[P \vert \vert Q] = 0, if and only if P = Q.
Proof: The discrete Kullback-Leibler divergence is defined as
\label{eq:KL} \mathrm{KL}[P||Q] = \sum_{x \in \mathcal{X}} p(x) \cdot \log \frac{p(x)}{q(x)} \; .The log sum inequality states that
\label{eq:logsum-ineq} \sum_{i=1}^n a_i \, \log_c \frac{a_i}{b_i} \geq a \, \log_c \frac{a}{b} \; .where a_1, \ldots, a_n and b_1, \ldots, b_n be non-negative real numbers and a = \sum_{i=1}^{n} a_i and b = \sum_{i=1}^{n} b_i. Because p(x) and q(x) are probability mass functions, such that
\label{eq:p-q-pmf} \begin{split} p(x) \geq 0, \quad \sum_{x \in \mathcal{X}} p(x) &= 1 \quad \text{and} \\ q(x) \geq 0, \quad \sum_{x \in \mathcal{X}} q(x) &= 1 \; , \end{split}theorem \eqref{eq:KL-nonneg} is simply a special case of \eqref{eq:logsum-ineq}, i.e.
\label{eq:KL-nonneg-qed} \mathrm{KL}[P||Q] \overset{\eqref{eq:KL}}{=} \sum_{x \in \mathcal{X}} p(x) \cdot \log \frac{p(x)}{q(x)} \overset{\eqref{eq:logsum-ineq}}{\geq} 1 \, \log \frac{1}{1} = 0 \; .∎
Sources: - Wikipedia (2020): "Log sum inequality"; in: Wikipedia, the free encyclopedia, retrieved on 2020-09-09; URL: https://en.wikipedia.org/wiki/Log_sum_inequality#Applications.
Metadata: ID: P166 | shortcut: kl-nonneg2 | author: JoramSoch | date: 2020-09-09, 07:02.