Here are examples with beta priors and binomial likelihoods.

Suppose the prior distribution of the heads probability $\theta$ is $\mathsf{Beta}(10,10)$ and that $n = 100$ tosses of a coin yield
$x = 47$ Heads. Then the posterior distribution of $\theta$ is
$\mathsf{Beta}(10 + x, 10 + 100 - x) \equiv
\mathsf{Beta}(57, 63).$

This results from Bayes' Theorem, multiplying prior $f(\theta)$ by likelihood $g(x|\theta)$ to
get posterior $h(\theta|x):$

$$f(\theta)\times g(x|\theta) \propto \theta^{10-1}(\theta)^{10-1}
\times \theta^{x}(1-\theta)^{n-x}\\
\propto h(\theta|x) \propto 
\theta^{(10+x)-1}(1-\theta)^{(10+100-x)-1}.$$

One could say that the prior distribution is 'effectively' equivalent
to advance knowledge of $20$ tosses of the coin yielding 10 heads.