For a real-valued random variable $X$, define its entropy by $H(X) = E[\phi(X)] - \phi(E[X])$, where $\phi(u) = u \log u$. It can be shown that, if the entropy satisfies the bound $$ H(e^{\lambda X}) \leq \frac{\sigma^2 \lambda^2}{2} E[e^{\lambda X}] $$ for all real $\lambda$, then $X$ must be a sub-Gaussian variable with parameter $\sigma$, by which I mean the following holds for all real $\lambda$: $$ E[e^{\lambda (X - E[X])}] \leq \exp \left( \frac{\sigma^2 \lambda^2}{2} \right) \text{.} $$ This is known as the Herbst argument.


Is the converse also true? i.e. do any sub-Gaussian variables satisfy the following entropy bound? $$ H(e^{\lambda X}) \leq \frac{\sigma^2 \lambda^2}{2} E[e^{\lambda X}] \quad \forall \lambda \in \mathbb{R} $$

  • 1
    $\begingroup$ It’s true up to a factor of 4, see e.g. Problem 3.12 of van Handel’s lecture notes on high-dimensional probability. $\endgroup$ Apr 1, 2023 at 19:11
  • $\begingroup$ @JasonGaitonde Thank you a lot for a nice reference! I will check it. $\endgroup$
    – aest
    Apr 3, 2023 at 7:32

1 Answer 1


As in the comment, the converse is true up to a factor of 4. The proof is given below.

Suppose $X$ is $\sigma^2/4$-subgaussian, i.e. the following holds for any $\lambda \in \mathbb{R}$: $$ \mathbb{E}[e^{\lambda X}] \leq \exp \left( \lambda \mathbb{E}[X] + \frac{\lambda^2 \sigma^2}{8} \right) \text{.} $$ Let $Z := e^{\lambda X} / \mathbb{E}[e^{\lambda X}]$. Then, one has \begin{align*} \mathbb{E}[Z \log Z] =& \mathbb{E} \left[ \frac{e^{\lambda X}}{\mathbb{E}[e^{\lambda X}]} \log \frac{e^{\lambda X}}{\mathbb{E}[e^{\lambda X}]} \right] \\=& \frac{1}{\mathbb{E}[e^{\lambda X}]} \mathbb{E} \left[ e^{\lambda X} \left( \log e^{\lambda X} - \log \mathbb{E}[e^{\lambda X}] \right) \right] \\=& \frac{1}{\mathbb{E}[e^{\lambda X}]} \left( \mathbb{E}[e^{\lambda X} \log e^{\lambda X}] - \mathbb{E}[e^{\lambda X}] \log \mathbb{E}[e^{\lambda X}] \right) \\=& \frac{H[e^{\lambda X}]}{\mathbb{E}[e^{\lambda X}]} \text{.} \end{align*} Moreover, note that $$ \mathbb{E}[Z \log Z] = \frac{\mathbb{E}[e^{\lambda X} \log Z]}{\mathbb{E}[e^{\lambda X}]} = \mathbb{E}_\lambda[\log Z] \text{,} $$ where $\mathbb{E}_\lambda[f(X)] := \mathbb{E}[e^{\lambda X} f(X)] / \mathbb{E}[e^{\lambda X}]$ denotes the Gibbs expectation. Now it suffices to show $\mathbb{E}_\lambda[\log Z] \leq \frac{\lambda^2 \sigma^2}{2}$.

The concavity of $\log$ and the Jensen inequality gives $$ \mathbb{E}_\lambda[\log Z] \leq \log \mathbb{E}_\lambda[Z] \text{.} $$ By the definition of the Gibbs expectation, we have \begin{align*} \mathbb{E}_\lambda[Z] =& \frac{\mathbb{E}_\lambda[e^{\lambda X}]}{\mathbb{E}[e^{\lambda X}]} \\=& \frac{\mathbb{E}[e^{2 \lambda X}] / \mathbb{E}[e^{\lambda X}]}{\mathbb{E}[e^{\lambda X}]} \\=& \frac{\mathbb{E}[e^{2 \lambda X}]}{\mathbb{E}[e^{\lambda X}]^2} \end{align*} Now the assumption gives $$ \mathbb{E}[e^{2 \lambda X}] \leq \exp \left(2 \lambda \mathbb{E}[X] + \frac{\lambda^2 \sigma^2}{2} \right) \text{,} $$ and the convexity of $\exp$ and the Jensen inequality yields $\mathbb{E}[e^{\lambda X}]^2 \geq e^{2 \lambda \mathbb{E}[X]}$. Hence, we have $$ \mathbb{E}_\lambda[\log Z] \leq \log \frac{\exp \left(2 \lambda \mathbb{E}[X] + \frac{\lambda^2 \sigma^2}{2}\right)}{\exp(2 \lambda \mathbb{E}[X])} = \frac{\lambda^2 \sigma^2}{2} $$ as desired.


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