Find the posterior of $\theta$. $\theta \sim \text{ Uniform}(0,1)$ and $X|\theta \sim \text{ Bernoulli}(\theta)$.
How would I find the posterior of $\theta$?

The likelihood of a Bernoulli is $p^{\sum{x_i}} (1-p)^{n-\sum x_i}$. How do I proceed? It is supposed to fit the beta distribution but I don't see how since it doesn't have a B-1
 A: I think what you call Bernoulli distribution should be called binomial distribution based on the context.
Let
\begin{align*}
h(\theta)=\begin{cases}1&\text{if $\theta\in(0,1)$,}\\0&\text{otherwise}\end{cases}
\end{align*}
denote the prior probability density function of $\theta$ and $f(\theta\,|\,X)$ denote the posterior probability density function. Using Bayes’s rule, one has for any $k\in\{0,1,\ldots,n\}$ that
\begin{align*}
f(\theta\,|\,X=k)=\frac{\mathbb P(X=k\,|\,\theta)\,h(\theta)}{\int \mathbb P(X=k\,|\,t)\,h(t)\,\mathrm dt}=\frac{\binom{n}{k}\theta^k(1-\theta)^{n-k}}{\int_0^1 \binom{n}{k}t^k(1-t)^{n-k}\,\mathrm dt}=\frac{\theta^k(1-\theta)^{n-k}}{\beta(k+1,n-k+1)}
\end{align*}
(where $\beta$ denotes the beta function), which is a beta distribution with parameters $k+1$ and $n-k+1$ supported on the interval $(0,1)$.
A: Probably you are given observations $X = \{X_1, \ldots, X_n\}$ with $X_i|\Theta = \theta \sim \text{ Bernoulli}(\theta)$, which gives the likelihood function of $\Theta$ as follows:
$$\ell(x|\Theta = \theta) = \theta^{\sum_{i = 1}^n x_i}(1 - \theta)^{n - \sum_{i = 1}^n x_i} = \theta^t(1 - \theta)^{n - t},$$
where $t = \sum_{i = 1}^n x_i$.
The prior of $\Theta$, by condition has pdf
$$\pi_\Theta(\theta) = I_{(0, 1)}(\theta).$$
Therefore the joint distribution of $X$ and $\theta$ is given by
$$p(x, \theta) = \ell(x|\theta)\pi_\Theta(\theta) = \theta^t(1 - \theta)^{n - t}I_{(0, 1)}(\theta)$$
Notice by Bayesian theorem, the posterior distribution of $\Theta$ given $X = x$ is
$$p_{\Theta|X}(\theta|x) = \frac{p(x, \theta)}{\int p(x, \theta) d\theta} \propto p(x, \theta). \tag{1}$$
where $\propto$ means two expressions differ only up to a constant which is independent of $\theta$.
It can be identified $p(x, \theta)$ is the pdf of $\text{Beta}(t + 1, n - t + 1)$ distribution, Thus by $(1)$, we claim that
$$\Theta|X = x \sim \text{ Beta}\left(\sum x_i + 1, n - \sum x_i + 1\right).$$
