This post shows that the binomial is indeed an exponential family with natural parameter $\text{logit}(\theta)$. Specifically, this exercise comes from Chapter 2 of Bayesian Data Analysis (BDA) 3rd Edition on page 37.

Recall that a binomial distribution whose likelihood $\Pr(y \mid \theta, n ) = \text{Bin}(y \mid n, \theta)$ with $n$ known, the conjugate prior distribution on $\theta$ is a beta distribution. Particularly, the likelihood (a binomial distribution) is

\(\begin{equation} \Pr( y \mid \theta ) \propto \theta^y (1 - \theta)^{n-y} \tag{1}\label{eq:likelihood} \end{equation}\)
with $\theta$ denotes a probability of a head occurrence, $n$ is a number of trials, and $y$ expresses a number of head occurences. Additionally, the prior (a beta distribution) is

\(\begin{equation} \Pr( \theta ) \propto \theta^{\alpha - 1} (1 - \theta)^{\beta - 1} \tag{2}\label{eq:prior} \end{equation}\)
with $\alpha$ and $\beta$ denote a number of head and tail occurrences respectively.

We will show that

\[\begin{equation} \Pr(\theta \mid y ) \propto g(\theta)^{\eta + n} \exp{\left( \phi(\theta)^T (\nu + t(y)) \right)} \tag{3}\label{eq:posterior-density} \end{equation}\]

Actually, Equation \eqref{eq:posterior-density} is a general form which holds for vector $\phi(\theta)$ and both $\eta$ and $\nu$ are constants. Let’s start computing the posterior density as follows:

\[\begin{align} \Pr(\theta \mid y ) &\propto \Pr(y \mid \theta) \Pr( \theta ) \tag{4}\label{eq:posterior-start} && \text{by Bayes Rule} \\ &\propto \theta^y (1- \theta)^{n-y} \, \theta^{\alpha - 1} (1 - \theta)^{\beta - 1} \\ &= \theta^{y+\alpha-1} (1 - \theta)^{n - y + \beta - 1} \\ &= \theta^{y+\alpha-1} \, \frac{1}{(1 - \theta)^{-n+y-\beta +1}} \\ &= \frac{\theta^{\alpha-1}}{(1 - \theta)^{-n-\beta+1}} \, \frac{\theta^y}{(1-\theta)^y} && \text{by rearranging terms} \\ &= \frac{\theta^{\alpha-1}}{(1 - \theta)^{-n-\beta+1}} \, \exp{\left( \log{ \left( \frac{\theta}{1-\theta} \right)^y } \right)} \\ &= \theta^{\alpha-1} (1 - \theta)^{\beta-1} (1 - \theta)^n \, \exp{( y \; \text{logit}{ (\theta) } )} \\ &= \left( \theta^{\frac{\alpha - 1}{n}} (1 - \theta)^{\frac{\beta-1}{n}} (1-\theta) \right)^n \, \exp{( \text{logit}{ (\theta) } \; y )} \\ &= g(\theta)^n \exp{( \phi(\theta) \; t(y) )} && \text{by referring to Equation }\eqref{eq:posterior-density} \end{align}\]

with $g(\theta) = \left( \theta^{\frac{\alpha - 1}{n}} (1 - \theta)^{\frac{\beta-1}{n}+1} \right)$, $t(y) = y$, and $\phi(\theta) = \text{logit}(\theta)$.
Finally, we have shown that the binomial is indeed an exponential family with natural parameter $\text{logit}(\theta)$.