A random variable \(X \sim \operatorname*{\mathbb P}\) is sub-Gaussian with variance factor \(\nu\) if \[\ln \operatorname*{\mathbb E}{\left[e^{\eta(X-\operatorname*{\mathbb E}[X])}\right]} ~\le~ \frac{\eta^2 \nu}{2} \qquad \text{for all $\eta \in \mathbb R$} .\] The reason for the name is that Gaussians satisfy this with equality. In this post we consider Bernoulli \(X \sim \mathcal B(\theta)\) (so \(X=1\) with probability \(\theta\) and \(X=0\) with probability \(1-\theta\)). Then \(\operatorname*{\mathbb E}[X] = \theta\). We find \begin{align} \notag \Psi_\theta(\eta) &~:=~ \ln \operatorname*{\mathbb E}{\left[e^{\eta(X-\theta)}\right]} \\ \notag &~=~ \ln {\left( \theta e^{\eta(1-\theta)} + (1-\theta) e^{\eta(0-\theta)} \right)} \\ \label{eq:show.ccv} &~=~ - \eta \theta + \ln {\left( 1 + \theta (e^\eta-1) \right)} \end{align} To get a bound that does not depend on \(\theta\), let us look at \[\Psi(\eta) ~:=~ \max_{\theta \in [0,1]} \Psi_\theta(\eta) .\] The expression for \(\Psi_\theta(\eta)\) implies that it is concave in \(\theta\). So it is maximised at zero derivative, i.e. \(\theta = \frac{1}{\eta } - \frac{1}{e^\eta -1}\), resulting in \begin{align*} \Psi(\eta) &~=~ - 1 + \frac{\eta}{e^{\eta }-1} - \ln \frac{\eta }{e^{\eta }-1} \end{align*} A series expansion reveals that \(\Psi(\eta) \approx \frac{\eta^2}{8}\) around \(\eta = 0\). It turns out (see e.g.  Proposition 4 in this earlier post) that this is an actual upper bound: \[\Psi(\eta) ~\le~ \frac{\eta^2}{8} \qquad \text{for all $\eta \in \mathbb R$} .\] This allows us to conclude that any Bernoulli variable is sub-Gaussian with variance factor \(\nu = \frac{1}{4}\).

Note that a careful application of Hoeffding’s Inequality (Cesa-Bianchi and Lugosi 2006 Lemma A.1.1) would also give this. We would observe that \(X-\theta \in [0-\theta, 1-\theta]\) and hence the width of the range is one. (The less careful application would say \(X-\theta \in [-1,1]\) of width two, and the \(\frac{1}{8}\) would become \(\frac{1}{2}\), resulting in variance factor of only \(\nu=1\).)

Cesa-Bianchi, Nicolò, and Gábor Lugosi. 2006. Prediction, Learning, and Games. Cambridge University Press.