Index: The Book of Statistical ProofsProbability Distributions ▷ Matrix-variate continuous distributions ▷ Matrix-normal distribution ▷ Differential entropy

Theorem: Let $X$ be an $n \times p$ random matrix following a matrix-normal distribution

$\label{eq:matn} X \sim \mathcal{MN}(M, U, V) \; .$

Then, the differential entropy of $X$ in nats is

$\label{eq:matn-dent} \mathrm{h}(X) = \frac{np}{2} \ln(2\pi) + \frac{n}{2} \ln|V| + \frac{p}{2} \ln|U| + \frac{np}{2} \; .$ $\label{eq:matn-mvn} X \sim \mathcal{MN}(M, U, V) \quad \Leftrightarrow \quad \mathrm{vec}(X) \sim \mathcal{N}(\mathrm{vec}(M), V \otimes U) \; ,$

and the differential entropy for the multivariate normal distribution in nats is

$\label{eq:mvn-dent} X \sim \mathcal{N}(\mu, \Sigma) \quad \Rightarrow \quad \mathrm{h}(X) = \frac{n}{2} \ln(2\pi) + \frac{1}{2} \ln|\Sigma| + \frac{1}{2} n$

where $X$ is an $n \times 1$ random vector.

Thus, we can plug the distribution parameters from \eqref{eq:matn} into the differential entropy in \eqref{eq:mvn-dent} using the relationship given by \eqref{eq:matn-mvn}

$\label{eq:matn-dent-s1} \mathrm{h}(X) = \frac{np}{2} \ln(2\pi) + \frac{1}{2} \ln|V \otimes U| + \frac{1}{2} np \; .$

Using the Kronecker product property

$\label{eq:kron-det} |A \otimes B| = |A|^m \, |B|^n \quad \text{where} \quad A \in \mathbb{R}^{n \times n} \quad \text{and} \quad B \in \mathbb{R}^{m \times m} \; ,$

the differential entropy from \eqref{eq:matn-dent-s1} becomes:

$\label{eq:matn-dent-s2} \begin{split} \mathrm{h}(X) &= \frac{np}{2} \ln(2\pi) + \frac{1}{2} \ln\left(|V|^n |U|^p\right) + \frac{1}{2} np \\ &= \frac{np}{2} \ln(2\pi) + \frac{n}{2} \ln|V| + \frac{p}{2} \ln|U| + \frac{np}{2} \; . \end{split}$
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Metadata: ID: P344 | shortcut: matn-dent | author: JoramSoch | date: 2022-09-22, 08:39.