Index: The Book of Statistical ProofsGeneral Theorems ▷ Information theory ▷ Kullback-Leibler divergence ▷ Invariance under parameter transformation

Theorem: The Kullback-Leibler divergence is invariant under parameter transformation, i.e.

$\label{eq:KL-inv} \mathrm{KL}[p(x)||q(x)] = \mathrm{KL}[p(y)||q(y)]$

where $y(x) = mx + n$ is an affine transformation of $x$ and $p(x)$ and $q(x)$ are the probability density functions of the probability distributions $P$ and $Q$ on the continuous random variable $X$.

Proof: The continuous Kullback-Leibler divergence (KL divergence) is defined as

$\label{eq:KL} \mathrm{KL}[p(x)||q(x)] = \int_{a}^{b} p(x) \cdot \log \frac{p(x)}{q(x)} \, \mathrm{d}x$

where $a = \mathrm{min}(\mathcal{X})$ and $b = \mathrm{max}(\mathcal{X})$ are the lower and upper bound of the possible outcomes $\mathcal{X}$ of $X$.

Due to the identity of the differentials

$\label{eq:diff} \begin{split} p(x) \, \mathrm{d}x &= p(y) \, \mathrm{d}y \\ q(x) \, \mathrm{d}x &= q(y) \, \mathrm{d}y \end{split}$

which can be rearranged into

$\label{eq:diff-dev} \begin{split} p(x) &= p(y) \, \frac{\mathrm{d}y}{\mathrm{d}x} \\ q(x) &= q(y) \, \frac{\mathrm{d}y}{\mathrm{d}x} \; , \end{split}$

the KL divergence can be evaluated as follows:

$\label{eq:MDE-DCE} \begin{split} \mathrm{KL}[p(x)||q(x)] &= \int_{a}^{b} p(x) \cdot \log \frac{p(x)}{q(x)} \, \mathrm{d}x \\ &= \int_{y(a)}^{y(b)} p(y) \, \frac{\mathrm{d}y}{\mathrm{d}x} \cdot \log \left( \frac{p(y) \, \frac{\mathrm{d}y}{\mathrm{d}x}}{q(y) \, \frac{\mathrm{d}y}{\mathrm{d}x}} \right) \, \mathrm{d}x \\ &= \int_{y(a)}^{y(b)} p(y) \cdot \log \frac{p(y)}{q(y)} \, \mathrm{d}y \\ &= \mathrm{KL}[p(y)||q(y)] \; . \end{split}$
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Metadata: ID: P115 | shortcut: kl-inv | author: JoramSoch | date: 2020-05-28, 00:18.