Index: The Book of Statistical ProofsGeneral Theorems ▷ Information theory ▷ Kullback-Leibler divergence ▷ Non-negativity

Theorem: The Kullback-Leibler divergence is always non-negative

\[\label{eq:KL-nonneg} \mathrm{KL}[P||Q] \geq 0\]

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.

\[\label{eq:KL-nonneg-qed} \mathrm{KL}[P||Q] \overset{\eqref{eq:KL}}{=} \sum_{x \in \mathcal{X}} p(x) \cdot \log \frac{p(x)}{q(x)} \overset{\eqref{eq:logsum-ineq}}{\geq} 1 \, \log \frac{1}{1} = 0 \; .\]

Metadata: ID: P166 | shortcut: kl-nonneg2 | author: JoramSoch | date: 2020-09-09, 07:02.