Index: The Book of Statistical ProofsProbability Distributions ▷ Multivariate continuous distributions ▷ Multivariate normal distribution ▷ Kullback-Leibler divergence

Theorem: Let $x$ be an $n \times 1$ random vector. Assume two multivariate normal distributions $P$ and $Q$ specifying the probability distribution of $x$ as

\[\label{eq:mvns} \begin{split} P: \; x &\sim \mathcal{N}(\mu_1, \Sigma_1) \\ Q: \; x &\sim \mathcal{N}(\mu_2, \Sigma_2) \; . \\ \end{split}\]

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

\[\label{eq:mvn-KL} \mathrm{KL}[P\,||\,Q] = \frac{1}{2} \left[ (\mu_2 - \mu_1)^T \Sigma_2^{-1} (\mu_2 - \mu_1) + \mathrm{tr}(\Sigma_2^{-1} \Sigma_1) - \ln \frac{|\Sigma_1|}{|\Sigma_2|} - n \right] \; .\]

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 multivariate normal distributions in \eqref{eq:mvns}, yields

\[\label{eq:mvn-KL-s1} \begin{split} \mathrm{KL}[P\,||\,Q] &= \int_{\mathbb{R}^n} \mathcal{N}(x; \mu_1, \Sigma_1) \, \ln \frac{\mathcal{N}(x; \mu_1, \Sigma_1)}{\mathcal{N}(x; \mu_2, \Sigma_2)} \, \mathrm{d}x \\ &= \left\langle \ln \frac{\mathcal{N}(x; \mu_1, \Sigma_1)}{\mathcal{N}(x; \mu_2, \Sigma_2)} \right\rangle_{p(x)} \; . \end{split}\]

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

\[\label{eq:mvn-KL-s2} \begin{split} \mathrm{KL}[P\,||\,Q] &= \left\langle \ln \frac{ \frac{1}{\sqrt{(2 \pi)^n |\Sigma_1|}} \cdot \exp \left[ -\frac{1}{2} (x-\mu_1)^\mathrm{T} \Sigma_1^{-1} (x-\mu_1) \right] }{ \frac{1}{\sqrt{(2 \pi)^n |\Sigma_2|}} \cdot \exp \left[ -\frac{1}{2} (x-\mu_2)^\mathrm{T} \Sigma_2^{-1} (x-\mu_2) \right] } \right\rangle_{p(x)} \\ &= \left\langle \frac{1}{2} \ln \frac{|\Sigma_2|}{|\Sigma_1|} - \frac{1}{2} (x-\mu_1)^\mathrm{T} \Sigma_1^{-1} (x-\mu_1) + \frac{1}{2} (x-\mu_2)^\mathrm{T} \Sigma_2^{-1} (x-\mu_2) \right\rangle_{p(x)} \\ &= \frac{1}{2} \left\langle \ln \frac{|\Sigma_2|}{|\Sigma_1|} - (x-\mu_1)^\mathrm{T} \Sigma_1^{-1} (x-\mu_1) + (x-\mu_2)^\mathrm{T} \Sigma_2^{-1} (x-\mu_2) \right\rangle_{p(x)} \; . \end{split}\]

Now, using the fact that $x = \mathrm{tr}(x)$, if $a$ is scalar, and the trace property $\mathrm{tr}(ABC) = \mathrm{tr}(BCA)$, we have:

\[\label{eq:mvn-KL-s3} \begin{split} \mathrm{KL}[P\,||\,Q] &= \frac{1}{2} \left\langle \ln \frac{|\Sigma_2|}{|\Sigma_1|} - \mathrm{tr}\left[ \Sigma_1^{-1} (x-\mu_1) (x-\mu_1)^\mathrm{T} \right] + \mathrm{tr}\left[ \Sigma_2^{-1} (x-\mu_2) (x-\mu_2)^\mathrm{T} \right] \right\rangle_{p(x)} \\ &= \frac{1}{2} \left\langle \ln \frac{|\Sigma_2|}{|\Sigma_1|} - \mathrm{tr}\left[ \Sigma_1^{-1} (x-\mu_1) (x-\mu_1)^\mathrm{T} \right] + \mathrm{tr}\left[ \Sigma_2^{-1} \left( x x^\mathrm{T} - 2 \mu_2 x^\mathrm{T} + \mu_2 \mu_2^\mathrm{T} \right) \right] \right\rangle_{p(x)} \; . \end{split}\]

Because trace function and expected value are both linear operators, the expectation can be moved inside the trace:

\[\label{eq:mvn-KL-s4} \begin{split} \mathrm{KL}[P\,||\,Q] &= \frac{1}{2} \left( \ln \frac{|\Sigma_2|}{|\Sigma_1|} - \mathrm{tr}\left[ \Sigma_1^{-1} \left\langle (x-\mu_1) (x-\mu_1)^\mathrm{T} \right\rangle_{p(x)} \right] + \mathrm{tr}\left[ \Sigma_2^{-1} \left\langle x x^\mathrm{T} - 2 \mu_2 x^\mathrm{T} + \mu_2 \mu_2^\mathrm{T} \right\rangle_{p(x)} \right] \right) \\ &= \frac{1}{2} \left( \ln \frac{|\Sigma_2|}{|\Sigma_1|} - \mathrm{tr}\left[ \Sigma_1^{-1} \left\langle (x-\mu_1) (x-\mu_1)^\mathrm{T} \right\rangle_{p(x)} \right] + \mathrm{tr}\left[ \Sigma_2^{-1} \left( \left\langle x x^\mathrm{T} \right\rangle_{p(x)} - \left\langle 2 \mu_2 x^\mathrm{T} \right\rangle_{p(x)} + \left\langle \mu_2 \mu_2^\mathrm{T} \right\rangle_{p(x)} \right) \right] \right) \; . \end{split}\]

Using the expectation of a linear form for the multivariate normal distribution

\[\label{eq:mvn-lfmean} x \sim \mathcal{N}(\mu, \Sigma) \quad \Rightarrow \quad \left\langle A x \right\rangle = A \mu\]

and the expectation of a quadratic form for the multivariate normal distribution

\[\label{eq:mvn-qfmean} x \sim \mathcal{N}(\mu, \Sigma) \quad \Rightarrow \quad \left\langle x^\mathrm{T} A x \right\rangle = \mu^\mathrm{T} A \mu + \mathrm{tr}(A \Sigma) \; ,\]

the Kullback-Leibler divergence from \eqref{eq:mvn-KL-s4} becomes:

\[\label{eq:mvn-KL-s5} \begin{split} \mathrm{KL}[P\,||\,Q] &= \frac{1}{2} \left( \ln \frac{|\Sigma_2|}{|\Sigma_1|} - \mathrm{tr}\left[ \Sigma_1^{-1} \Sigma_1 \right] + \mathrm{tr}\left[ \Sigma_2^{-1} \left( \Sigma_1 + \mu_1 \mu_1^\mathrm{T} - 2 \mu_2 \mu_1^\mathrm{T} + \mu_2 \mu_2^\mathrm{T} \right) \right] \right) \\ &= \frac{1}{2} \left( \ln \frac{|\Sigma_2|}{|\Sigma_1|} - \mathrm{tr}\left[ I_n \right] + \mathrm{tr}\left[ \Sigma_2^{-1} \Sigma_1 \right] + \mathrm{tr}\left[ \Sigma_2^{-1} \left( \mu_1 \mu_1^\mathrm{T} - 2 \mu_2 \mu_1^\mathrm{T} + \mu_2 \mu_2^\mathrm{T} \right) \right] \right) \\ &= \frac{1}{2} \left( \ln \frac{|\Sigma_2|}{|\Sigma_1|} - n + \mathrm{tr}\left[ \Sigma_2^{-1} \Sigma_1 \right] + \mathrm{tr}\left[ \mu_1^\mathrm{T} \Sigma_2^{-1} \mu_1 - 2 \mu_1^\mathrm{T} \Sigma_2^{-1} \mu_2 + \mu_2^\mathrm{T} \Sigma_2^{-1} \mu_2 \right] \right) \\ &= \frac{1}{2} \left[ \ln \frac{|\Sigma_2|}{|\Sigma_1|} - n + \mathrm{tr}\left[ \Sigma_2^{-1} \Sigma_1 \right] + (\mu_2 - \mu_1)^T \Sigma_2^{-1} (\mu_2 - \mu_1) \right] \; . \end{split}\]

Finally, rearranging the terms, we get:

\[\label{eq:mvn-KL-qed} \mathrm{KL}[P\,||\,Q] = \frac{1}{2} \left[ (\mu_2 - \mu_1)^T \Sigma_2^{-1} (\mu_2 - \mu_1) + \mathrm{tr}(\Sigma_2^{-1} \Sigma_1) - \ln \frac{|\Sigma_1|}{|\Sigma_2|} - n \right] \; .\]
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Metadata: ID: P92 | shortcut: mvn-kl | author: JoramSoch | date: 2020-05-05, 06:57.