Index: The Book of Statistical ProofsProbability DistributionsUnivariate continuous distributions ▷ Normal distribution ▷ Kullback-Leibler divergence

Theorem: Let $X$ be a random variable. Assume two normal distributions $P$ and $Q$ specifying the probability distribution of $X$ as

$\label{eq:norms} \begin{split} P: \; X &\sim \mathcal{N}(\mu_1, \sigma_1^2) \\ Q: \; X &\sim \mathcal{N}(\mu_2, \sigma_2^2) \; . \end{split}$

Then, the Kullback-Leibler divergence of $P$ from $Q$ is given by

$\label{eq:norm-KL} \mathrm{KL}[P\,||\,Q] = \frac{1}{2} \left[ \frac{(\mu_2 - \mu_1)^2}{\sigma_2^2} + \frac{\sigma_1^2}{\sigma_2^2} - \ln \frac{\sigma_1^2}{\sigma_2^2} - 1 \right] \; .$

Proof: The KL divergence for a continuous random variable is given by

$\label{eq:KL-cont} \mathrm{KL}[P\,||\,Q] = \int_{\mathcal{X}} p(x) \, \ln \frac{p(x)}{q(x)} \, \mathrm{d}x$

which, applied to the normal distributions in \eqref{eq:norms}, yields

$\label{eq:norm-KL-s1} \begin{split} \mathrm{KL}[P\,||\,Q] &= \int_{-\infty}^{+\infty} \mathcal{N}(x; \mu_1, \sigma_1^2) \, \ln \frac{\mathcal{N}(x; \mu_1, \sigma_1^2)}{\mathcal{N}(x; \mu_2, \sigma_2^2)} \, \mathrm{d}x \\ &= \left\langle \ln \frac{\mathcal{N}(x; \mu_1, \sigma_1^2)}{\mathcal{N}(x; \mu_2, \sigma_2^2)} \right\rangle_{p(x)} \; . \end{split}$

Using the probability density function of the normal distribution, this becomes:

$\label{eq:norm-KL-s2} \begin{split} \mathrm{KL}[P\,||\,Q] &= \left\langle \ln \frac{ \frac{1}{\sqrt{2 \pi} \sigma_1} \cdot \exp \left[ -\frac{1}{2} \left( \frac{x-\mu_1}{\sigma_1} \right)^2 \right] }{ \frac{1}{\sqrt{2 \pi} \sigma_2} \cdot \exp \left[ -\frac{1}{2} \left( \frac{x-\mu_2}{\sigma_2} \right)^2 \right] } \right\rangle_{p(x)} \\ &= \left\langle \ln \left( \sqrt \frac{\sigma_2^2}{\sigma_1^2} \cdot \exp\left[ -\frac{1}{2} \left( \frac{x-\mu_1}{\sigma_1} \right)^2 + \frac{1}{2} \left( \frac{x-\mu_2}{\sigma_2} \right)^2 \right] \right) \right\rangle_{p(x)} \\ &= \left\langle \frac{1}{2} \ln \frac{\sigma_2^2}{\sigma_1^2} -\frac{1}{2} \left( \frac{x-\mu_1}{\sigma_1} \right)^2 + \frac{1}{2} \left( \frac{x-\mu_2}{\sigma_2} \right)^2 \right\rangle_{p(x)} \\ &= \frac{1}{2} \left\langle - \left( \frac{x-\mu_1}{\sigma_1} \right)^2 + \left( \frac{x-\mu_2}{\sigma_2} \right)^2 - \ln \frac{\sigma_1^2}{\sigma_2^2} \right\rangle_{p(x)} \\ &= \frac{1}{2} \left\langle - \frac{(x-\mu_1)^2}{\sigma_1^2} + \frac{x^2 - 2 \mu_2 x + \mu_2^2}{\sigma_2^2} - \ln \frac{\sigma_1^2}{\sigma_2^2} \right\rangle_{p(x)} \; . \end{split}$

Because the expected value is a linear operator, the expectation can be moved into the sum:

$\label{eq:norm-KL-s3} \begin{split} \mathrm{KL}[P\,||\,Q] &= \frac{1}{2} \left[ - \frac{\left\langle (x-\mu_1)^2 \right\rangle}{\sigma_1^2} + \frac{\left\langle x^2 - 2 \mu_2 x + \mu_2^2 \right\rangle}{\sigma_2^2} - \left\langle \ln \frac{\sigma_1^2}{\sigma_2^2} \right\rangle \right] \\ &= \frac{1}{2} \left[ - \frac{\left\langle (x-\mu_1)^2 \right\rangle}{\sigma_1^2} + \frac{\left\langle x^2 \right\rangle - \left\langle 2 \mu_2 x \right\rangle + \left\langle \mu_2^2 \right\rangle}{\sigma_2^2} - \ln \frac{\sigma_1^2}{\sigma_2^2} \right] \; . \end{split}$

The first expectation corresponds to the variance

$\label{eq:var} \left\langle (X-\mu)^2 \right\rangle = \mathrm{E}[(X-\mathrm{E}(X))^2] = \mathrm{Var}(X)$ $\label{eq:norm-var} X \sim \mathcal{N}(\mu, \sigma^2) \quad \Rightarrow \quad \mathrm{Var}(X) = \sigma^2 \; .$

Additionally applying the raw moments of the normal distribution

$\label{eq:norm-mom-raw} X \sim \mathcal{N}(\mu, \sigma^2) \quad \Rightarrow \quad \left\langle x \right\rangle = \mu \quad \text{and} \quad \left\langle x^2 \right\rangle = \mu^2 + \sigma^2 \; ,$

the Kullback-Leibler divergence in \eqref{eq:norm-KL-s3} becomes

$\label{eq:norm-KL-s4} \begin{split} \mathrm{KL}[P\,||\,Q] &= \frac{1}{2} \left[ - \frac{\sigma_1^2}{\sigma_1^2} + \frac{\mu_1^2 + \sigma_1^2 - 2 \mu_2 \mu_1 + \mu_2^2}{\sigma_2^2} - \ln \frac{\sigma_1^2}{\sigma_2^2} \right] \\ &= \frac{1}{2} \left[ \frac{\mu_1^2 - 2 \mu_1 \mu_2 + \mu_2^2}{\sigma_2^2} + \frac{\sigma_1^2}{\sigma_2^2} - \ln \frac{\sigma_1^2}{\sigma_2^2} - 1 \right] \\ &= \frac{1}{2} \left[ \frac{(\mu_1 - \mu_2)^2}{\sigma_2^2} + \frac{\sigma_1^2}{\sigma_2^2} - \ln \frac{\sigma_1^2}{\sigma_2^2} - 1 \right] \end{split}$

which is equivalent to \eqref{eq:norm-KL}.

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Metadata: ID: P193 | shortcut: norm-kl | author: JoramSoch | date: 2020-11-19, 07:08.